NVIDIA-Nemotron-3.5-Super-VL-120B-A12B — NIM model artifacts

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$\"Benchmark$

Description

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B is a vision-language model (VLM) trained by NVIDIA. This repository holds the model artifacts consumed by the NVIDIA Nemotron 3.5 Super VL NIM (2.0.13): the BF16 weights and the NVFP4 weights. The NIM selects the version that matches the GPU profile it runs.

The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has 120B total parameters and 12B active parameters and supports context lengths up to 1M tokens. Multi-Token Prediction (MTP) enables native speculative decoding.

This model is available for commercial and non-commercial use under the OpenMDW-1.1 license.

For more details on how to deploy and use the model — see the Quick Start Guide below!

Versions in This Repository

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Version	Model	Files	Size	Used by
`bf16-ga-20261003`	`NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16`	81	231.84 GB	BF16 NIM profiles
`nvfp4-ga-20261003`	`NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4`	107	69.91 GB	NVFP4 NIM profiles

Each version ships a checksums.blake3 file. Other versions in this repository are earlier pre-release builds and are not used by the 2.0.13 NIM.

Model Summary

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Total Parameters	120B (12B active)
Architecture	Mamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder
Precision	BF16 (`bf16-ga-20261003`), NVFP4 (`nvfp4-ga-20261003`)
Inputs / Outputs	Text, images, and video / text
Context Length	Up to 1M tokens
Minimum GPU	NVFP4: 1× H200 / 1× B200 / 1× GB200 / 1× B300 / 1× GB300, DGX Station, DGX Spark. BF16: 2× B200 / 2× GB200 / 2× B300 / 2× GB300
Supported Hardware	NVIDIA Blackwell (B200, GB200, B300, GB300, RTX Pro 6000 Blackwell Server Edition, DGX Station, DGX Spark); NVIDIA Hopper (H100, H200); NVIDIA Ampere (A100-80GB)
Supported Languages	English, French, German, Italian, Japanese, Spanish, and Chinese
Speculative Decoding	Native Multi-Token Prediction (MTP) — Read more below
Reasoning Mode	Configurable through the chat template (`enable_thinking=True/False`)
Recommended Sampling	Text: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95
Best For	Efficient deployment of text and vision-language agents
License	OpenMDW-1.1
Release Date	10/15/2026 (NIM 2.0.13)

Hardware Matrix

Number of GPUs per NIM profile for each combination of precision and hardware.

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Hardware	BF16	NVFP4
H100 SXM 80GB	8	2
H200 SXM	4	1
B200	2	1
GB200	2	1
B300	2	1
GB300	2	1
A100 SXM4 80GB	Not Available	2
RTX Pro 6000 Blackwell Server Edition	Not Available	2
DGX Station (GB300)	Not Available	1
DGX Spark (GB10)	Not Available	1

Speculative Decoding

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Profile	Method	Artifact
All profiles	Built-in MTP head	included in the BF16 / NVFP4 weights

Speculative decoding is lossless: verified tokens are identical to non-speculative decoding.

Model Overview

Model Developer: NVIDIA Corporation

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

License/Terms of Use

Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1.

Benchmarks

Agentic Coding Benchmarks

The following results evaluate the post-trained Nemotron 3.5 Super model.

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Benchmark	Agent Harness	Score (%)
SWE-bench Pro	Claude Code	49.45
Terminal-Bench 2.1	Codex	61.80

These are the highest scores across six evaluated agent harnesses, using their standard configurations and unmodified system prompts. For SWE-bench Pro, Git history is removed and the user instruction prohibits seeking online resources. Results depend on the harness and its configuration.

Deployment Geography: Global

Use Case

This model is intended for developers deploying vision-language agents for coding, search, tool-assisted workflows, document question answering, and image and video understanding, on NVIDIA data center and DGX Spark systems.

Release Date

Hugging Face 10/15/2026 via Hugging Face BF16 and Hugging Face NVFP4

Reference(s)

Model Architecture

Architecture Type: Mamba-2–Transformer hybrid Mixture of Experts (MoE) with Multi-Token Prediction (MTP)
Network Architecture: Nemotron Hybrid MoE with 512 routed experts per MoE layer, a C-RADIO vision encoder, and a vision-language connector
Number of Model Parameters: 120B total / 12B active

Model Design

The model combines sparse expert computation, attention, and Mamba-2 layers to support long-context processing. The vision encoder and connector turn visual inputs into representations processed by the language backbone. A shared MTP head predicts future tokens recursively for speculative decoding.

The model supports up to 1M tokens of context. The prompt, visual inputs, conversation history, tool results, and generated response share this context window. Individual deployment recipes may use a smaller context window to accommodate the available GPU memory and concurrency.

Training Methodology

Stage 1: Foundation and Multi-Token Prediction

The language backbone is initialized from Nemotron 3 Super Base. A shared MTP head is trained for 100B tokens while the backbone remains frozen, followed by 1T tokens of distillation from Nemotron 3 Ultra Base.

Stage 2: Continued Pre-Training and Mid-Training

The model undergoes 100B tokens of Indic continued pre-training, a 35B-token extension to 1M context, 67B tokens of software-engineering mid-training at 256K context, and a final 9B-token long-context recovery stage. Software-engineering data includes repositories, issue–pull-request pairs, commit diffs, build and CI logs, and agent trajectories.

Stage 3: Vision-Language Training

A C-RADIO vision encoder and connector extend the language backbone to visual inputs, including images, document pages, and video frames.

Stage 4: Supervised Fine-Tuning

A two-stage supervised fine-tuning pipeline jointly trains the language and vision components on vision, reasoning, code, tools, chat, safety, and long-context data. Training uses packed sequences of up to 524,288 tokens.

Stage 5: Reinforcement Learning and Distillation

Reinforcement learning with verifiable rewards, reinforcement learning from human feedback, and specialized teacher training improve reasoning, agentic, chat, and vision capabilities. Multi-teacher on-policy distillation consolidates these capabilities into a single model.

Stage 6: MTP Boosting

The MTP head is further trained against the frozen post-trained backbone to improve speculative-decoding acceptance.

Input

Input Type(s): Text, images, and video
Input Format(s): Text strings, images, and sampled video frames\n
- Video: mp4, up to 2 minutes.
- Image: Red, Green, Blue (RGB) (jpeg, png)
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Input Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Input: Maximum context length up to 1M tokens. Documents can be supplied as page images with accompanying text.

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-dimensional sequences
Other Properties Related to Output: Maximum context length up to 1M tokens.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): vLLM (NVIDIA NIM)
Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere - A100-80GB; NVIDIA Blackwell - B200, GB200, B300, GB300, RTX Pro 6000 Blackwell Server Edition, DGX Station, DGX Spark; NVIDIA Hopper - H100, H200
Preferred/Supported Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

bf16-ga-20261003 (10/03/2026)
nvfp4-ga-20261003 (10/03/2026)

Quick Start Guide

These artifacts are downloaded and served by the NVIDIA Nemotron 3.5 Super VL NIM; you do not need to download them manually. Set NIM_IMAGE to the NIM container image for the release.

\nexport NIM_IMAGE=\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p "$LOCAL_NIM_CACHE"\n\">

export NGC_API_KEY=<your NGC API key>\nexport NIM_IMAGE=<NIM container image>\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p \"$LOCAL_NIM_CACHE\"\n

1. List the profiles and pick the one for your GPU. Pin it with NIM_MODEL_PROFILE; automatic selection is not reliable across GPU SKUs.

docker run --rm --gpus all -e NGC_API_KEY $NIM_IMAGE list-model-profiles\n

2. Start the NIM.

\\\n -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video \\\n -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \\\n -p 8000:8000 \\\n $NIM_IMAGE\n\">

docker run -it --rm --gpus all --shm-size=16GB \\\n  -e NGC_API_KEY \\\n  -e NIM_MODEL_PROFILE=<profile id from step 1> \\\n  -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video \\\n  -v \"$LOCAL_NIM_CACHE:/opt/nim/.cache\" \\\n  -p 8000:8000 \\\n  $NIM_IMAGE\n

NVIDIA_DRIVER_CAPABILITIES must include video so the NIM can decode video on the GPU (NVDEC).

3. Check readiness.

curl -s http://localhost:8000/v1/health/ready\ncurl -s http://localhost:8000/v1/models\n

API Client

The examples below use the OpenAI-compatible client against the NIM endpoint. Recommended sampling settings are temperature 1.0, top_p 0.95 for text and temperature 0.6, top_p 0.95 for image and video.

Copy the identifier returned by GET /v1/models into MODEL below.

from openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\nMODEL = \"model\"  # replace with the identifier returned by GET /v1/models\n

NVIDIA Nemotron 3.5 Super VL exposes reasoning control through chat-template kwargs: thinking enabled (the default), and thinking disabled for direct answers. With the OpenAI SDK these are passed via extra_body, not as top-level arguments.

Text, image, and video examples: Click to expand!

Text

response = client.chat.completions.create(\n    model=MODEL,\n    messages=[{\"role\": \"user\", \"content\": \"Hello, what can you do?\"}],\n    max_tokens=16000,\n    temperature=1.0,\n    top_p=0.95,\n)\nprint(response.choices[0].message.content)\n

Expected output:

Hello! I am an AI assistant designed to help you with a wide variety of tasks. Here are some of the things I can do:\n\n*   **Answer Questions:** I can provide information on a vast range of topics, from science and history to current events and general knowledge.\n*   **Writing & Editing:** I can help write emails, essays, poems, scripts, or articles. I can also proofread and improve your existing text.\n*   **Coding & Tech Support:** I can help write code in various programming languages, debug errors, and explain technical concepts.\n*   **Summarization:** I can condense long articles, documents, or videos into key points.\n*   **Translation:** I can translate text between many different languages.\n*   **Brainstorming:** I can help generate ideas for projects, business names, gifts, or creative endeavors.\n*   **Math & Logic:** I can solve mathematical problems and work through logic puzzles.\n\nIs there something specific you would like help with today?\n

Reasoning is on by default. The NIM returns the chain of thought in the reasoning field of the message, and content holds the final answer. Add extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}} for a direct answer.

Image

str:\n with open(path, "rb") as f:\n b64 = base64.b64encode(f.read()).decode("utf-8")\n return f"data:image/jpeg;base64,{b64}"\n\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[\n {\n "role": "user",\n "content": [\n {"type": "image_url", "image_url": {"url": image_to_data_url("your_image.jpg")}},\n {"type": "text", "text": "Describe this image."},\n ],\n }\n ],\n max_tokens=16000,\n temperature=0.6,\n top_p=0.95,\n extra_body={"chat_template_kwargs": {"enable_thinking": False}},\n)\nprint(response.choices[0].message.content)\n\">

import base64\n\ndef image_to_data_url(path: str) -> str:\n    with open(path, \"rb\") as f:\n        b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n    return f\"data:image/jpeg;base64,{b64}\"\n\nresponse = client.chat.completions.create(\n    model=MODEL,\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"image_url\", \"image_url\": {\"url\": image_to_data_url(\"your_image.jpg\")}},\n                {\"type\": \"text\", \"text\": \"Describe this image.\"},\n            ],\n        }\n    ],\n    max_tokens=16000,\n    temperature=0.6,\n    top_p=0.95,\n    extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}},\n)\nprint(response.choices[0].message.content)\n

Expected output:

The image shows a capybara in a zoo enclosure. The capybara is eating green leafy vegetables on the ground. Its enclosure has a dirt floor and a large hollowed-out log behind it. There's a concrete wall visible, with trees and bushes beyond it. The capybara is facing right, with its head down as it eats. The scene is well-lit, suggesting it's daytime. The image captures a natural moment of the capybara's daily life in its zoo habitat.\n

Video

str:\n with open(path, "rb") as f:\n b64 = base64.b64encode(f.read()).decode("utf-8")\n return f"data:video/mp4;base64,{b64}"\n\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[\n {\n "role": "user",\n "content": [\n {"type": "video_url", "video_url": {"url": video_to_data_url("your_video.mp4")}},\n {"type": "text", "text": "Describe what happens in this video."},\n ],\n }\n ],\n max_tokens=16000,\n temperature=0.6,\n top_p=0.95,\n extra_body={"chat_template_kwargs": {"enable_thinking": False}},\n)\nprint(response.choices[0].message.content)\n\">

import base64\n\ndef video_to_data_url(path: str) -> str:\n    with open(path, \"rb\") as f:\n        b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n    return f\"data:video/mp4;base64,{b64}\"\n\nresponse = client.chat.completions.create(\n    model=MODEL,\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": [\n                {\"type\": \"video_url\", \"video_url\": {\"url\": video_to_data_url(\"your_video.mp4\")}},\n                {\"type\": \"text\", \"text\": \"Describe what happens in this video.\"},\n            ],\n        }\n    ],\n    max_tokens=16000,\n    temperature=0.6,\n    top_p=0.95,\n    extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}},\n)\nprint(response.choices[0].message.content)\n

Expected output:

The video shows a rotating view of Earth from space, highlighting the illuminated areas of the planet against the dark backdrop of space. The continents and oceans are visible, with city lights indicating populated regions. The rotation provides a dynamic perspective of the Earth's surface, showcasing its curvature and the distribution of light across different landmasses.\n

For video use cases, try both enabling and disabling thinking for best results. A public https:// video URL also works in place of the base64 data URL.

PDF input: The API accepts images, not raw PDF files. Render each page to PNG (e.g. with pymupdf) and send it as a base64 image_url as in the Image example above.

Recommended Media Inputs

Video

Use MP4 files of 1–2 minutes. Every NIM profile samples at 2 FPS, up to 256 frames, with Efficient Video Sampling (pruning rate 0.5), which covers roughly two minutes of footage. Longer clips are truncated to 256 frames.

Images

Tested input configurations:

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Input type	Resolution	Images per request
Simple image	512×512	1
Document image	1536×1024	1
Multi-document image	1536×1024	32

Each page image counts toward the prompt side of the context length.

Notes

Video container and length: MP4, 1–2 minutes. Other containers are untested.
Video decoding needs NVDEC access: start the container with NVIDIA_DRIVER_CAPABILITIES=compute,utility,video.
max_tokens vs context length: max_tokens caps only the completion. Prompt plus completion must fit inside the profile's context length, and video prefill consumes a large share of it.

Training, Testing, and Evaluation Datasets

Training

Data Modality: Text, images, and video
\nFoundation Training Data Size: More than 20 trillion tokens\nImage Training Data Size: 1 Million to 1 Billion Images\nText Training Data Size: More than 10 Trillion Tokens\nVideo Training Data Size: 10,000 to 1 Million Hours\nFoundation Data Collection Period: 2013 to December 2025
\nData Collection Method: Hybrid: automated, manually-collected, and synthetic
\nLabeling Method: Hybrid: automated, manually-labeled, and synthetic

The foundation corpus contains curated and synthetic text covering English, 19 other spoken languages, and 43 programming languages. Sources include webpages, dialogue, articles, code, and other written materials across math, science, legal, finance, and general knowledge domains.

The post-training corpus includes reasoning, coding, tool use, instruction following, structured outputs, chat, safety, and long-context tasks, together with image and video understanding. Synthetic trajectories are filtered for structural correctness, repetition, and task validity.

For more information about the datasets used to train this model, please see the Nemotron 3.5 Super VL EU Training Content Summary.

Detailed dataset information

Base Pre-Training Corpus (Nemotron 3 Foundation)

The foundation of the model is trained on the Nemotron 3 corpus, comprising the following datasets from the Nemotron Pretraining Datasets collection:

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Dataset Collection	Token Counts	Description
Nemotron-CC-v2 & v2.1	9.1T	A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content.
Nemotron-CC-Code-v1	427.9B	High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations.
Nemotron-Pretraining-Code-v1 & v2 & v3	1.7T	Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data.
Nemotron-CC-Math-v1	133.3B	High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures.
Nemotron-Pretraining-Specialized-v1 & v1.1 & v1.2 & Nemotron-Pretraining-SFT-v1	660.0B	Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding.
Nemotron-Pretraining-Legal-v1	4.3B	Synthetic datasets targeting the legal domain.

Public Datasets

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Dataset	Collection Period
GSM8K	4/23/2025
CC-NEWS	4/23/2025
Common Crawl	4/23/2025
Wikimedia	4/23/2025
Bespoke-Stratos-17k	4/23/2025
tigerbot-kaggle-leetcodesolutions-en-2k	4/23/2025
glaive-function-calling-v2	4/23/2025
APIGen Function-Calling	4/23/2025
LMSYS-Chat-1M	4/23/2025
Open Textbook Library - CC BY-SA & GNU subset and OpenStax - CC BY-SA subset	4/23/2025
Advanced Reasoning Benchmark, tigerbot-kaggle-leetcodesolutions-en-2k, PRM800K, and SciBench	4/23/2025
FineWeb-2	4/23/2025
Court Listener	Legacy Download
peS2o	Legacy Download
OpenWebMath	Legacy Download
BioRxiv	Legacy Download
PMC Open Access Subset	Legacy Download
OpenWebText2	Legacy Download
Stack Exchange Data Dump	Legacy Download
PubMed Abstracts	Legacy Download
NIH ExPorter	Legacy Download
arXiv	Legacy Download
BigScience Workshop Datasets	Legacy Download
SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)	Legacy Download
Advanced Mathematical Problem Solving	Legacy Download
MathPile	Legacy Download
NuminaMath CoT	Legacy Download
PMC Article	Legacy Download
FLAN	Legacy Download
Advanced Reasoning Benchmark	Legacy Download
SciBench	Legacy Download
WikiTableQuestions	Legacy Download
FinQA	Legacy Download
Riddles	Legacy Download
Problems in Elementary Mathematics for Home Study	Legacy Download
MedMCQA	Legacy Download
Cosmos QA	Legacy Download
MCTest	Legacy Download
AI2's Reasoning Challenge	Legacy Download
OpenBookQA	Legacy Download
MMLU Auxiliary Train	Legacy Download
social-chemestry-101	Legacy Download
Moral Stories	Legacy Download
The Common Pile v0.1	Legacy Download
FineMath	Legacy Download
MegaMath	Legacy Download
MultiverseMathHard	10/2/2025
SWE-Gym	10/2/2025
WorkBench	10/2/2025
WildChat-1M	10/2/2025
OpenCodeReasoning-2	10/2/2025
HelpSteer3	10/2/2025
opc-sft-stage2	10/2/2025
Big-Math-RL-Verified	10/2/2025
MetaMathQA	10/2/2025
simple-arithmetic-problems	10/2/2025
arithmetic	10/2/2025
Skywork-OR1-RL-Data	10/2/2025
FastChat	10/2/2025
News Commentary	10/2/2025
Essential-Web	10/2/2025
finepdfs	10/2/2025
HotpotQA	10/2/2025
SQuAD2.0	10/2/2025
NLTK Words Lists	10/2/2025

Crawled and Scraped from Online Sources by NVIDIA

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways used in the Nemotron-CC data pipeline. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set.

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Dataset	Modality	Dataset Size	Collection Period	Collecting Organisation
English Common Crawl	Text	3.36T	4/8/2025	NVIDIA Advanced Deep Learning Research
English Common Crawl 1.1	Text	Not disclosed	10/2/2025	NVIDIA Advanced Deep Learning Research
Multilingual Common Crawl	Text	812.7B	5/1/2025	NVIDIA Advanced Deep Learning Research
GitHub Crawl	Text	747.4B	4/29/2025	NVIDIA Advanced Deep Learning Research
GitHub Crawl 1.1	Text	172.7B	9/30/2025	NVIDIA Advanced Deep Learning Research
Sangraha	Text	109,263,115 indexed source sequences: 77,231,480 verified and 32,031,635 unverified	Undisclosed	NVIDIA Advanced Deep Learning Research
LITA Chunked Video Labels, Custom No-Thinking Dense Captions	Text (video labels)	151,349 records	Undisclosed	NVIDIA Advanced Deep Learning Research
LITA Chunked Video Labels, Cleaned v2	Text (video labels)	150,434 records	Undisclosed	NVIDIA Advanced Deep Learning Research

Private Non-publicly Accessible Datasets of Third Parties

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Dataset	Model(s) used
Global Regulation	Unknown
TAUS Translation Memory	Unknown
Scale HLE	Unknown
HackerRank Coding	Unknown
RL data for Search	Gemini 3; GPT-5
Mercor SWE-AgentsV1	Undisclosed
KeenSight Batch 1, Relabeled Subset	Qwen3.5-122B-A10B
KeenSight Batch 1, Remaining Subset	Qwen3.5-122B-A10B
Camio Multilingual OCR and Document Layout	Undisclosed
Chinese Visual Question Answering, Relabeled Subset	Qwen3.5-122B-A10B
Camio Chinese OCR, Cleaned Variant	Qwen3.5-122B-A10B
Camio English OCR, Cleaned Variant	Qwen3.5-122B-A10B
KeenSight Batch 2, Remaining Subset	Qwen3.5-122B-A10B
KeenSight Batch 1, Safety-Filtered Trace Variant	Qwen3.5-122B-A10B
Chinese Visual Question Answering, Safety-Filtered Trace Variant	Qwen3.5-122B-A10B
Camio Chinese Question Answering, Verified-Correct Subset	Qwen3.5
KeenSight Chinese OCR, Filtered Subset	DeepSeek-OCR
Camio Synthetic Trading-Graph Question Answering	Undisclosed
Camio Synthetic Menu and Receipt Question Answering	Qwen3.5-122B-A10B
Camio Chinese OCR, Formatted Variant	Qwen3.5-122B-A10B
Camio Synthetic Time-Series Question Answering	Undisclosed
Camio English OCR, Formatted Variant	Qwen3.5-122B-A10B
Lean Formal Proofs — RL	Undisclosed
Document-Grounded Finance Reasoning, Patch Tasks — RL	Undisclosed
Math CoT — SFT	Kimi K3
Chat — SFT	Undisclosed
Chat, Reasoning Off — SFT	Undisclosed
Conversational Tool-Use Simulation — RL	Undisclosed
Math CoT, Low Variant — SFT	Kimi K3
Document-Grounded Finance Reasoning — SFT	Undisclosed
Safety Preference v0.3 — RL	Undisclosed
Response-Length Instruction Following, Reasoning Off — SFT	Undisclosed
Math Python TIR, Low Variant — SFT	Kimi K3
Math Python TIR — SFT	Kimi K3
General-Purpose Tool Calling — SFT	Qwen3.5-397B-A17B; Gemma-4-31B-it
Knowledge Agent — SFT	Undisclosed
DataFunnel arXiv Science4 v1.0 — RL	DeepSeek-V4-Flash (problem extraction); GLM-5.2 (refinement); Kimi K3 and K3 V5 (verification and filtering); Super 3.5 r14 (difficulty profiling); Qwen3-235B-A22B-Instruct-2507-FP8 (reward judging)
Structured Outputs v3 — RL	gpt-oss-120b
Response-Length Instruction Following — SFT	Undisclosed
Filtered CUDA Coding — SFT	Undisclosed
Reasoning Gym — RL	Undisclosed
Competitive Coding Challenges v2 — SFT	GLM-5.2
Multi-Turn Instruction Following, Reasoning Off — SFT	Undisclosed
Multi-Turn Instruction Following — SFT	Undisclosed
Instruction Following, Filtered RLHF Prompts — SFT	Undisclosed
Citation Formatting — RL	gpt-oss-120b
Long-Context General Data — SFT	Undisclosed
Free-Form Formatting — RL	gpt-oss-120b
Structured Outputs v4 — RL	Undisclosed
Tool-Call Schema — RL	No model used for RL conversion; source-generation models undisclosed
Competitive Coding, OCR Python — SFT	DeepSeek-V4-Pro
Lean Formal Proofs — SFT	gpt-oss-120b; Goedel-Prover-V2-32B; Qwen2.5-32B-Instruct
Citation Instruction Following — RL	Undisclosed
Search Graph Walks — SFT	MiniMax-M2
General-Purpose Tool Calling, Reasoning Off — SFT	Undisclosed
Knowledge Agent Tau3, Reasoning Off — SFT	Undisclosed
ARC-AGI Puzzle Reasoning — SFT	Kimi-K2.5; Qwen3-235B-A22B-Thinking-2507; DeepSeek-V3.2; Qwen3-Next-80B-A3B-Thinking; GLM-4.5-Air-FP8; gpt-oss-120b; GLM-4.7-FP8; Ring-1T; Ministral-3-14B-Reasoning-2512
Calendar — RL	Undisclosed
ACEProof — SFT	Undisclosed
Long Translation — SFT	Undisclosed
Agentless Software Engineering — SFT	Undisclosed
Long-Context PDF Translation v1.0 — SFT	DeepSeek-V4-Flash (translation generation); GPT-5.6 Sol (filtering and judging)

Private Non-publicly Accessible Datasets by NVIDIA

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Dataset	Model(s) used
Simple Minesweeper	Undisclosed
Simple Sudoku	Undisclosed
Multitool Typewriter Hard	Undisclosed
Machine Translation of News Commentary and TAUS Translation Memory	Undisclosed
Machine Translation of STEM -	Qwen2.5-14B-Instruct
Competitive Coding RL data from Nemotron Cascade	Undisclosed
Long context RL	Undisclosed
Single-step SWE RL for patch generation	Undisclosed
OpenHands SWE	Undisclosed

NVIDIA-Sourced Synthetic Datasets (Pre-Training)

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Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Nemotron-Pretraining-Fact-Seeking	Text	35.0B	FineWiki	Qwen3-30B-A3B-Instruct-2507
Nemotron-Pretraining-Legal	Text	4.3B	CommonPile (caselaw_access_project_filtered); California Code of Regulations; Judicial Ethics Opinions; GLOBALCIT; CUAD; Nemotron Personas; ToSDR Terms of Service Corpus; CodeHima/TOS_Dataset; ContractNLI; CaseHOLD; Code of Federal Regulations; Canadian Case Law (subsets that allow commercial use)	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Formal-Logic	Text	128M	Nemotron Personas	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Economics	Text	73.4M	-	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Multiple-Choice	Text	1.6B	MMLU Auxiliary Train	DeepSeek-V3; Qwen3-235B-A22B
Nemotron-Pretraining-Code-Concepts	Text	7.3B	-	gpt-oss-20b; gpt-oss-120b
Nemotron-Pretraining-Unconditional-Algorithmic	Text	196.5M	-	gpt-oss-120b; Qwen3-235B-A22B
More Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B	Text	1.1B	train splits of acp_bench; ai2_arc; babi; gsm8k; hendrycks_math; IFEval; MedText; mediqa_qa; mlqa; MMLU-Pro; mmlu-pro-plus; MMLU-ProX; nq_open; tinyGSM8k; truthful_qa; truthfulqa-multi; MATH-lighteval; mmlu; awesome-chatgpt-prompts; super_glue	DeepSeek v3; Qwen3-235B-A22B
Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B	Text	6.7B	train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP	DeepSeek v3; Qwen3-235B-A22B
Synthetic Art of Problem Solving from DeepSeek-R1	Text	40B	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	DeepSeek-R1
Synthetic Moral Stories and Social Chemistry from Qwen3-235B-A22B-Thinking-2507 and Mixtral-8x22B-v0.1	Text	15.2M	social-chemestry-101; Moral Stories	Qwen3-235B-A22B-Thinking-2507; Mixtral-8x22B-v0.1
Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1	Text	327M	social-chemestry-101; Moral Stories	Mixtral-8x22B-v0.1
Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	83.6M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	9.7M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B	Text	175M	OpenStax - CC BY-SA subset; GSM8K; Open Textbook Library - CC BY-SA & GNU subset	DeepSeek-R1, DeepSeek-V3; DeepSeek-V3-0324; Qwen2.5-72B
Nemotron-PrismMath	Text	4.6B	Big-Math-RL-Verified; OpenR1-Math-220k	Qwen2.5-0.5B-instruct, Qwen2.5-72B-Instruct; DeepSeek-R1-Distill-Qwen-32B
Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	350M	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Rephrased Math Data from Common Crawl from phi-4	Text	73B	Common Crawl	phi-4
Synthetic Math Data from Common Crawl 4plus	Text	52.3B	Common Crawl	phi-4
Synthetic Math Data from Common Crawl 3	Text	80.9B	Common Crawl	phi-4
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324	Text	4.0B	AQUA-RAT; LogiQA; AR-LSAT	DeepSeek-V3; DeepSeek-V3-0324
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B	Text	4.2B	AQUA-RAT; LogiQA; AR-LSAT	Qwen3-30B-A3B
Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; GSM8K; PRM800K	Qwen2.5-32B-Instruct; Qwen2.5-Math-72B; Qwen2.5-Math-7B; Qwen2.5-72B-Instruct
Synthetic MMLU Auxiliary Train from DeepSeek-R1	Text	0.5B	MMLU Auxiliary Train	DeepSeek-R1
Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	Undisclosed	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct	Text	415.8B	Common Crawl	Qwen3-30B-A3B; Mistral-NeMo-12B-Instruct
Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct	Text	Undisclosed	-	Nemotron-4-340B-Instruct
Synthetic Common Crawl Code from phi-4	Text	427.9B	Common Crawl	phi-4
Synthetic Scientific Coding from Qwen3-235B-A22B	Text	1.2B	Wikimedia	Qwen3-235B-A22B
Tool Calling Data	Text	26.2B	-	Qwen3-235B-A22B-2507; gpt-oss-120b
Synthetic Essential-Web from QwQ-32B	Text	28.1B	Essential-Web	QwQ-32B
Translated Synthetic Crawl	Text	389.9B	Common Crawl	Qwen3-30B-A3B
Translated Synthetic Wikipedia	Text	7.9B	Wikimedia	Qwen3-30B-A3B
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507
Synthetic Search STEM OPENQ from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528	Text	Undisclosed	-	QwQ-32B; Qwen3-30B-A3B; Qwen3-235B-A22B; Qwen3-235B-A22B-Instruct-2507; Mistral-Small-3.1-24B-Instruct-2503; Mistral-Small-3.2-24B-Instruct-2506; MiniMax-M1-80k; MiniMax-M1-40k; Kimi-K2-Instruct; DeepSeek-V3-0324; DeepSeek-R1-0528
Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B	Text	Undisclosed	WildChat-1M; arena-human-preference-140k	DeepSeek-R1; gemma-2-2b-it; gemma-3-27b-it; gpt-oss-20b; gpt-oss-120b; Mistral-7B-Instruct-v0.3; Mixtral-8x22B-Instruct-v0.1; Nemotron-4-340B-Instruct; NVIDIA-Nemotron-Nano-9B-v2; Phi-4-mini-instruct; Phi-3-small-8k-instruct; Phi-3-medium-4k-instruct; Qwen3-235B-A22B; QwQ-32B
Synthetic Code from Qwen3-32B	Text	Undisclosed	English Common Crawl; English Common Crawl 1.1	Qwen3-32B
Synthetic OpenCodeReasoning from DeepSeek-R1	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1
Synthetic OpenCodeReasoning from DeepSeek-R1-0528	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1-0528
Synthetic HackerRank Coding from DeepSeek-R1-0528	Text	Undisclosed	HackerRank Coding Dataset	DeepSeek-R1-0528
Synthetic LIMO from DeepSeek-R1-0528	Text	Undisclosed	LIMO	DeepSeek-R1-0528
Synthetic SCP from DeepSeek-R1-0528	Text	Undisclosed	SCP-116K	DeepSeek-R1-0528
Synthetic Stack Exchange from DeepSeek-R1-0528	Text	Undisclosed	Stack Exchange	DeepSeek-R1-0528
Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Stack Exchange from gpt-oss-120b	Text	Undisclosed	Stack Exchange	gpt-oss-120b
Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Wikipedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	Essential-Web	Qwen3-30B-A3B; Qwen3-235B-A22B-Thinking-2507
Synthetic Essential-Web from gpt-oss-120b	Text	Undisclosed	Essential-Web	gpt-oss-120b
Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4	Text	Undisclosed	Common Crawl; FineMath	Qwen3-30B-A3B; Qwen3-235B-A22B; phi-4
Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528	Text	Undisclosed	Magicoder-Evol-Instruct-110K; opc-sft-stage2; TACO; OpenCodeReasoning; OpenMathReasoning; NuminaMath CoT	DeepSeek-R1; DeepSeek-R1-0528
Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	-	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	OpenMathReasoning	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic KernelBook from DeepSeek-R1-0528	Text	Undisclosed	KernelBook	DeepSeek-R1-0528
Synthetic Scale HLE from gpt-oss-120b	Text	Undisclosed	Scale HLE	gpt-oss-120b
Synthetic CDQuestions from gpt-oss-120b	Text	Undisclosed	CDQuestions	gpt-oss-120b
Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Vedantu from gpt-oss-120b	Text	Undisclosed	Vedantu	gpt-oss-120b
Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ10 from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528; Qwen3-235B-A22B-Instruct-2507

NVIDIA-Sourced Synthetic Datasets (Post-Training)

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Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Synthetic Single-Step Tool Use — RL	Text	Approximately 1,433,203 samples	Handwritten prompts; no external seed data	DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B
Synthetic Sparse Tables with Mixed Prompts	Text and images	100,000 records	Generated in-house; no external seed data	None; generated without an SDG model
Synthetic Single-Step Tool Use — RL	Text	Approximately 1,433,203 samples	Handwritten prompts; no external seed data	DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B
Synthetic Sparse Tables with Mixed Prompts	Text and images	100,000 records	Generated in-house; no external seed data	None; generated without an SDG model
Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Hermes Agent Reasoning Traces	Text	Undisclosed	[lambda/hermes-agent-reasoning-traces]	[hermes-agent-generator]
Synthetic Competitive Coding from DeepSeek-V4-Pro	Text	Undisclosed	[NVCompetitiveCodingV1]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive MATH CoT and TIR from Nemotron 5.5	Text	Undisclosed	[Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[Nemotron 5.5]
Vendor Terminal Bench-like Tasks from Mercor	Text	Undisclosed	[Terminal bench like tasks curated by the vendor]	[Undisclosed - purchased dataset]
Turing Math Data Pack	Text	Undisclosed	[Turing Math Data Pack dataset]	[Undisclosed - purchased dataset]
Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5	Text	Undisclosed	[DocQA-RL-1.6K]; [DAPO-Math-17k]	[GPT-5.5]
Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K	Text	Undisclosed	[DocQA-RL-1.6K]	Undisclosed
Synthetic Competitive Coding Gym Tasks	Text	Undisclosed	[NVCompetitiveCodingV1.1]	Undisclosed
Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3	Text	Undisclosed	[SEC filings from sec.gov]	[GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct]
Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[Nemotron-RL-agent-structured-outputs-v1]	[Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1	Text	Undisclosed	[Long-context SFT data]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1]
Synthetic Science RL Data Blend from Qwen2.5-32B	Text	Undisclosed	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[Qwen2.5-32B]
Synthetic Abstention Data from Nemotron Super v3	Text	Undisclosed	[Go abstention Dataset]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Chemistry Data from Nemotron Super v3	Text	Undisclosed	[ChemData 700K]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Tool Call Schema for RL	Text	469,983	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Freeform Text Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Synthetic Citation Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Droid Harness Pivot Vendor Data	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed
Synthetic HotpotQA Training Data from Qwen3-235B	Text	Undisclosed	[HotpotQA]	[Qwen3-235B]
Synthetic Natural Language Math Proofs from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[Nemotron 5.5]
Synthetic Stack Overflow OpenQ	Text	Undisclosed	[Pile-FreeLaw]	Undisclosed
Chemistry Ether0 Vendor Data	Text	Undisclosed	[Chemistry ether0 vendor data]	Undisclosed
Synthetic Litmus-Bench Chemistry from ChEMBL	Text	Undisclosed	[ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit]	Undisclosed
Synthetic ZINC Chemistry from Nemotron Super v3	Text	Undisclosed	[ZINC]	[Nemotron Super v3]
ARC-AGI Gym Environment	Text	Undisclosed	[ARC-AGI-2]	[ARC-AGI-2]
Synthetic Agentic Search Tool-Use from DeepSeek-V3.2	Text	Undisclosed	[Mercor Data]	[DeepSeek-V3.2]
Synthetic Text-To-SQL	Text	96,564	[In-house Text-to-SQL data]	[gpt-oss-120b]
Dialog Memory Vendor Data	Text	Undisclosed	[Patronus external vendor agreement]	Undisclosed
Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct	Text	Undisclosed	[In-house indirect prompt injection data]	[nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct]
Synthetic Malicious Code and Agentic Security	Text	Undisclosed	[In-house malicious-code / agentic-security data]	Undisclosed
Synthetic Single-Step SWE Patch Selection	Text	Undisclosed	[SWE-Gym Dataset]; [SWE Bench Verified Benchmark]	[ground truth and task checks]
Synthetic Natural Language Math Final Answers from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[nemotron 5.5]
Synthetic Simple Math Prompts for Token Efficiency	Text	Undisclosed	[In-house simple math prompts]	Undisclosed
Synthetic Abstention Data from Nemotron Super v3 (CRAG)	Text	Undisclosed	[CRAG]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Agentless SWE	Text	242,536	[SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench]	[openai/gpt-oss-120b]
Synthetic Agentless SWE from DeepSeek-R1-0528	Text	209,976	[SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith]	[deepseek-ai/DeepSeek-R1-0528]
Synthetic Agentic CUDA Traces from GLM-4.7	Text	2,276	[Internal CUDA task data]	[GLM-4.7]
Synthetic Math Proofs from DeepSeek-V3.2-Speciale	Text	820,772	[Nemotron-Math-Proofs-v1]	[SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation]
Synthetic Multilingual SFT from DeepSeek-V3	Text	1,245,284	[Nano v3 SFT data]	[DeepSeek-V3]
Synthetic Agentic Code from gpt-oss-120b	Text	109,086	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]	[openai/gpt-oss-120b]
Synthetic Agentic CLI and Web Skills from gpt-oss-120b	Text	27,418	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic Agentic Coding from gpt-oss-120b	Text	160,531	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic OpenCode Agentic Tasks from gpt-oss-120b	Text	614,773	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic SWE Unverified	Text	Undisclosed	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash]
Synthetic ARC-AGI Ultra Data	Text	192,016	[ARC-AGI-2]; [arc dataset collection]	[ARC-AGI-2]
Synthetic LiveCodeBench TIR from DeepSeek-R1-0528	Text	1,283,398	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B	Text	1,233,247	[Verilog/SystemVerilog seed code]	[SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet]
Synthetic Aider Python Tasks from DeepSeek-R1-0528	Text	236,099	[Exercism (GitHub Python)]	[Deepseek R1 0528]
Synthetic Chat Reasoning-Off Data from GLM-5	Text	646,738	[lmarena-ai/repochat-arena-preference-4k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Chat Reasoning-On Data from GLM-5	Text	644,286	[lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1	Text	132,067	[Safety SFT Data: Ultra]	[nvidia/Riva-Translate-4B-Instruct-v1.1]
Synthetic Science Reasoning Effort Medium	Text	502,722	[science-reasoning-effort-medium-v0]	Undisclosed
Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b	Text	12,455	[Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2]	[gpt-oss-120b]
Synthetic Terminal Bench Data from OpenReasoningv2	Text	Undisclosed	[OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K]	[OpenReasoningv2]
Synthetic Tulu Instruction Following from DeepSeek-R1-0528	Text	105,361	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Instruction Following from gpt-oss-120b	Text	151,988	[IFEval]; [IFEvalG]	[gpt-oss-120b]
Synthetic Instruction Following for RL	Text	Undisclosed	[WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507	Text	25,992	[Hand-written prompts]	[Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Terminus Ultra Agentic Reasoning Blend	Text	96,881	[ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith]	[DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8]
Synthetic STEM from Qwen3-235B-A22B-Thinking-2507	Text	1,174,694	[IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning]	[Qwen3-235B-A22B-Thinking-2507]
Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b	Text	Undisclosed	[arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD]	[Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b]
Translation Data from TAUS	Text	1,618,055	[TAUS proprietary dataset]	Undisclosed
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B	Text	860,469	[Nemotron-Math-Proofs-v1]	[Goedel-Prover-V2-32B]
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b	Text	1,201,815	[Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange]	[gpt-oss-120b]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct	Text	Undisclosed	[Nano-V3 SFT Data (without tool call)]	[Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage)	Text	Undisclosed	[Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces	[DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B]
Synthetic Search Graph Walk	Text	6,977	[Wikidata / Wikipedia KnowledgeBase]	[MiniMaxAI/MiniMax-M2]
Synthetic Agentic Diverse Domains	Text	281,537	[Handwritten prompts (synthetic; no external seed data used)]	[SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	[Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B]
Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale	Text	1,900,553	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2-Speciale]
Synthetic Nemotron Math TIR from DeepSeek-V3.2	Text	1,789,258	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2]
Synthetic NemoCascade OCR Distillation from gpt-oss-120b	Text	682,864	[Nemotron-X training datasets]	[gpt-oss-120b]
Synthetic CUDA 100k	Text	93,086	[KernelBook]; [HuggingFace Transformers]; [FlashInfer]	[gpt-oss-120b]; [DeepSeek-R1-0528]
Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2	Text	30,358	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science HLE with Python from GPT-OSS and Kimi-K2	Text	85,184	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search and Python from GPT-OSS and Kimi-K2	Text	6,179	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search from GPT-OSS and Kimi-K2	Text	32,554	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507	Text	326,700	[SEC filings]	[GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507]
Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2	Text	532,942	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2	Text	131,045	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2	Text	2,085,600	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Text-To-SQL from gpt-oss-120b	Text	Undisclosed	[In-house Text-to-SQL data]	[gpt-oss-120b]
Synthetic Tool Call Schema for RL (extended)	Text	707,967	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b	Text	44,091	[Safety SFT Data]	[google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b]
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1	Text	Undisclosed	[Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA]	[DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1]
Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking	Text	Undisclosed	[ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA]	[Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking]
Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	NVIDIA Internal	[gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking	Text	Undisclosed	[DocFinQA]; [SWE-smith]	[Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking]
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[DeepSeek-R1-0528]
Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B	Text	Undisclosed	[Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-8B]
Vendor Terminal Bench-like Tasks (Droid)	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed

Language Distribution in Post-Training

For our post-training recipe, we focused on the following languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese. Those languages were represented in the form of multilingual reasoning and translation tasks.

Additional Training Data

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Data	Composition and Purpose
Indic continued pre-training	100B tokens: 10B selected Indic tokens and 90B tokens from the preceding blend.
Software-engineering mid-training	67B tokens: whole repositories (20%), issue–pull-request pairs (41%), commit diffs (3%), build and CI logs (1%), agent traces (10%), pre-training replay (20%), and long-context data (5%). Sources are decontaminated against SWE-bench and Terminal-Bench repositories.
Long-context pre-training	A 35B-token extension to 1M context, followed by a 9B-token long-context recovery stage.
Vision-language data	Image and video data from the Nemotron 3 Nano Omni training corpus, including visual question answering, charts, tables, OCR, multi-page documents, visual grounding, and video understanding.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Post-Training Data	Composition and Purpose
Additional synthetic vision data	Chart understanding, grounded perception, multimodal reasoning, and visual tool-use trajectories, including data generated with Qwen3.5 and Kimi models.
Reasoning trajectories	Mathematics and STEM responses generated with Kimi K3; mathematical proofs generated with DeepSeek V4 Pro; competitive-programming trajectories generated with GLM-5.2.
Chat and safety	Refreshed chat responses from Qwen, Kimi, and Gemma models, with additional safety data.
Long-context supervised data	512K-context data, synthetic graph reasoning and transduction tasks, long-horizon search and coding trajectories, and documents containing up to hundreds of pages.
Long-document translation	Chunked translations of PG19 and FinePDFs-Edu documents generated with DeepSeek V4 Flash.

Testing Datasets

Data Collection Method: Hybrid: automated, manually-collected, and synthetic
\nLabeling Method: Hybrid: automated, manually-labeled, and synthetic
\nProperties: Standard benchmarks and task-specific test suites covering reasoning, coding, tool use, instruction following, long context, and visual understanding.

Evaluation Datasets

Inference

Acceleration Engine: vLLM (NVIDIA NIM)
Test Hardware:\n
- NVIDIA Ampere\n
  - 2x A100-80GB
  \n
- NVIDIA Blackwell\n
  - 1-2x B200, 1-2x GB200
  - 1-2x B300, 1-2x GB300
  - 2x RTX Pro 6000 Blackwell Server Edition
  - DGX Station
  - DGX Spark
  \n
- NVIDIA Hopper\n
  - 2-8x H100
  - 1-4x H200
  \n
\n

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\n \n $\"Pre-Training$ \n \n \n $\"Homepage\"$ \n \n \n $\"Discord\"$ \n \n

\n\n

\n \n $\"License\"$ \n \n

\n\n![Benchmark accuracy and maximum output throughput](data:image/png;base64,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)\n\n## Description\n\n**NVIDIA-Nemotron-3.5-Super-VL-120B-A12B** is a vision-language model (VLM) trained by NVIDIA. This repository holds the model artifacts consumed by the **NVIDIA Nemotron 3.5 Super VL NIM (2.0.13)**: the BF16 weights and the NVFP4 weights. The NIM selects the version that matches the GPU profile it runs.\n\nThe model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has **120B total parameters** and **12B active parameters** and supports context lengths up to **1M tokens**. Multi-Token Prediction (MTP) enables native speculative decoding.\n\nThis model is available for commercial and non-commercial use under the OpenMDW-1.1 license.\n\nFor more details on how to deploy and use the model — see the [Quick Start Guide](#quick-start-guide) below!\n\n## Versions in This Repository\n\n| Version | Model | Files | Size | Used by |\n|---|---|---|---|---|\n| `bf16-ga-20261003` | `NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16` | 81 | 231.84 GB | BF16 NIM profiles |\n| `nvfp4-ga-20261003` | `NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4` | 107 | 69.91 GB | NVFP4 NIM profiles |\n\nEach version ships a `checksums.blake3` file. Other versions in this repository are earlier pre-release builds and are not used by the 2.0.13 NIM.\n\n## Model Summary\n\n| | |\n|:---|:---|\n| **Total Parameters** | 120B (12B active) |\n| **Architecture** | Mamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder |\n| **Precision** | BF16 (`bf16-ga-20261003`), NVFP4 (`nvfp4-ga-20261003`) |\n| **Inputs / Outputs** | Text, images, and video / text |\n| **Context Length** | Up to 1M tokens |\n| **Minimum GPU** | NVFP4: 1× H200 / 1× B200 / 1× GB200 / 1× B300 / 1× GB300, DGX Station, DGX Spark. BF16: 2× B200 / 2× GB200 / 2× B300 / 2× GB300 |\n| **Supported Hardware** | NVIDIA Blackwell (B200, GB200, B300, GB300, RTX Pro 6000 Blackwell Server Edition, DGX Station, DGX Spark); NVIDIA Hopper (H100, H200); NVIDIA Ampere (A100-80GB) |\n| **Supported Languages** | English, French, German, Italian, Japanese, Spanish, and Chinese |\n| **Speculative Decoding** | Native Multi-Token Prediction (MTP) — [Read more below](#speculative-decoding) |\n| **Reasoning Mode** | Configurable through the chat template (`enable_thinking=True/False`) |\n| **Recommended Sampling** | Text: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95 |\n| **Best For** | Efficient deployment of text and vision-language agents |\n| **License** | [OpenMDW-1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) |\n| **Release Date** | 10/15/2026 (NIM 2.0.13) |\n\n

Hardware Matrix

\n\nNumber of GPUs per NIM profile for each combination of precision and hardware.\n\n| Hardware | BF16 | NVFP4 |\n|---|---|---|\n| **H100 SXM 80GB** | 8 | 2 |\n| **H200 SXM** | 4 | 1 |\n| **B200** | 2 | 1 |\n| **GB200** | 2 | 1 |\n| **B300** | 2 | 1 |\n| **GB300** | 2 | 1 |\n| **A100 SXM4 80GB** | Not Available | 2 |\n| **RTX Pro 6000 Blackwell Server Edition** | Not Available | 2 |\n| **DGX Station (GB300)** | Not Available | 1 |\n| **DGX Spark (GB10)** | Not Available | 1 |\n\n

\n\n## Speculative Decoding\n\n| Profile | Method | Artifact |\n|---|---|---|\n| All profiles | Built-in **MTP** head | included in the BF16 / NVFP4 weights |\n\nSpeculative decoding is lossless: verified tokens are identical to non-speculative decoding.\n\n## Model Overview\n\n**Model Developer:** NVIDIA Corporation\n\n### What is Nemotron?\n\nNVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.\n\n## License/Terms of Use\n\n**Governing Download Terms:** Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1).\n\n## Benchmarks\n\n### Agentic Coding Benchmarks\n\nThe following results evaluate the post-trained Nemotron 3.5 Super model.\n\n| Benchmark | Agent Harness | Score (%) |\n|:---|:---|---:|\n| SWE-bench Pro | Claude Code | 49.45 |\n| Terminal-Bench 2.1 | Codex | 61.80 |\n\nThese are the highest scores across six evaluated agent harnesses, using their standard configurations and unmodified system prompts. For SWE-bench Pro, Git history is removed and the user instruction prohibits seeking online resources. Results depend on the harness and its configuration.\n\n### Deployment Geography: Global\n\n### Use Case\n\nThis model is intended for developers deploying vision-language agents for coding, search, tool-assisted workflows, document question answering, and image and video understanding, on NVIDIA data center and DGX Spark systems.\n\n### Release Date\n\nHugging Face 10/15/2026 via [Hugging Face BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16) and [Hugging Face NVFP4](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4)
\n\n## Reference(s)\n\n* [NVIDIA Nemotron 3 model family on Hugging Face](https://huggingface.co/collections/nvidia/)\n* [NVIDIA Nemotron 3 Ultra Technical Report](https://research.nvidia.com/labs/nemotron/files/)\n\n## Model Architecture\n\n- **Architecture Type:** Mamba-2–Transformer hybrid Mixture of Experts (MoE) with Multi-Token Prediction (MTP)\n- **Network Architecture:** Nemotron Hybrid MoE with 512 routed experts per MoE layer, a C-RADIO vision encoder, and a vision-language connector\n- **Number of Model Parameters:** 120B total / 12B active\n\n## Model Design\n\nThe model combines sparse expert computation, attention, and Mamba-2 layers to support long-context processing. The vision encoder and connector turn visual inputs into representations processed by the language backbone. A shared MTP head predicts future tokens recursively for speculative decoding.\n\nThe model supports up to 1M tokens of context. The prompt, visual inputs, conversation history, tool results, and generated response share this context window. Individual deployment recipes may use a smaller context window to accommodate the available GPU memory and concurrency.\n\n## Training Methodology\n\n**Stage 1: Foundation and Multi-Token Prediction**\n\nThe language backbone is initialized from Nemotron 3 Super Base. A shared MTP head is trained for 100B tokens while the backbone remains frozen, followed by 1T tokens of distillation from Nemotron 3 Ultra Base.\n\n**Stage 2: Continued Pre-Training and Mid-Training**\n\nThe model undergoes 100B tokens of Indic continued pre-training, a 35B-token extension to 1M context, 67B tokens of software-engineering mid-training at 256K context, and a final 9B-token long-context recovery stage. Software-engineering data includes repositories, issue–pull-request pairs, commit diffs, build and CI logs, and agent trajectories.\n\n**Stage 3: Vision-Language Training**\n\nA C-RADIO vision encoder and connector extend the language backbone to visual inputs, including images, document pages, and video frames.\n\n**Stage 4: Supervised Fine-Tuning**\n\nA two-stage supervised fine-tuning pipeline jointly trains the language and vision components on vision, reasoning, code, tools, chat, safety, and long-context data. Training uses packed sequences of up to 524,288 tokens.\n\n**Stage 5: Reinforcement Learning and Distillation**\n\nReinforcement learning with verifiable rewards, reinforcement learning from human feedback, and specialized teacher training improve reasoning, agentic, chat, and vision capabilities. Multi-teacher on-policy distillation consolidates these capabilities into a single model.\n\n**Stage 6: MTP Boosting**\n\nThe MTP head is further trained against the frozen post-trained backbone to improve speculative-decoding acceptance.\n\n## Input\n\n- **Input Type(s):** Text, images, and video\n- **Input Format(s):** Text strings, images, and sampled video frames\n - Video: mp4, up to 2 minutes. \n - Image: Red, Green, Blue (RGB) (jpeg, png)\n- **Input Parameters:** One-Dimensional (1D): Sequences\n- **Other Properties Related to Input:** Maximum context length up to 1M tokens. Documents can be supplied as page images with accompanying text.\n\n## Output\n\n- **Output Type(s):** Text\n- **Output Format:** String\n- **Output Parameters:** One-dimensional sequences\n- **Other Properties Related to Output:** Maximum context length up to 1M tokens.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration\n\n- **Runtime Engine(s):** vLLM (NVIDIA NIM)\n- **Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere - A100-80GB; NVIDIA Blackwell - B200, GB200, B300, GB300, RTX Pro 6000 Blackwell Server Edition, DGX Station, DGX Spark; NVIDIA Hopper - H100, H200\n- **Preferred/Supported Operating System(s):** Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## Model Version(s)\n\n* `bf16-ga-20261003` (10/03/2026)\n* `nvfp4-ga-20261003` (10/03/2026)\n\n## **Quick Start Guide**\n\nThese artifacts are downloaded and served by the NVIDIA Nemotron 3.5 Super VL NIM; you do not need to download them manually. Set `NIM_IMAGE` to the NIM container image for the release.\n\n```shell\nexport NGC_API_KEY=\nexport NIM_IMAGE=\nexport LOCAL_NIM_CACHE=~/.cache/nim\nmkdir -p \"$LOCAL_NIM_CACHE\"\n```\n\n**1. List the profiles and pick the one for your GPU.** Pin it with `NIM_MODEL_PROFILE`; automatic selection is not reliable across GPU SKUs.\n\n```shell\ndocker run --rm --gpus all -e NGC_API_KEY $NIM_IMAGE list-model-profiles\n```\n\n**2. Start the NIM.**\n\n```shell\ndocker run -it --rm --gpus all --shm-size=16GB \\\n -e NGC_API_KEY \\\n -e NIM_MODEL_PROFILE= \\\n -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video \\\n -v \"$LOCAL_NIM_CACHE:/opt/nim/.cache\" \\\n -p 8000:8000 \\\n $NIM_IMAGE\n```\n\n`NVIDIA_DRIVER_CAPABILITIES` must include `video` so the NIM can decode video on the GPU (NVDEC).\n\n**3. Check readiness.**\n\n```shell\ncurl -s http://localhost:8000/v1/health/ready\ncurl -s http://localhost:8000/v1/models\n```\n\n### **API Client**\n\nThe examples below use the OpenAI-compatible client against the NIM endpoint. Recommended sampling settings are **temperature 1.0, top_p 0.95** for text and **temperature 0.6, top_p 0.95** for image and video.\n\nCopy the identifier returned by `GET /v1/models` into `MODEL` below.\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\nMODEL = \"model\" # replace with the identifier returned by GET /v1/models\n```\n\nNVIDIA Nemotron 3.5 Super VL exposes reasoning control through chat-template kwargs: thinking enabled (the default), and thinking disabled for direct answers. With the OpenAI SDK these are passed via `extra_body`, not as top-level arguments.\n\n

Text, image, and video examples: Click to expand!

\n\n**Text**\n\n```python\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[{\"role\": \"user\", \"content\": \"Hello, what can you do?\"}],\n max_tokens=16000,\n temperature=1.0,\n top_p=0.95,\n)\nprint(response.choices[0].message.content)\n```\n\nExpected output:\n\n```text\nHello! I am an AI assistant designed to help you with a wide variety of tasks. Here are some of the things I can do:\n\n* **Answer Questions:** I can provide information on a vast range of topics, from science and history to current events and general knowledge.\n* **Writing & Editing:** I can help write emails, essays, poems, scripts, or articles. I can also proofread and improve your existing text.\n* **Coding & Tech Support:** I can help write code in various programming languages, debug errors, and explain technical concepts.\n* **Summarization:** I can condense long articles, documents, or videos into key points.\n* **Translation:** I can translate text between many different languages.\n* **Brainstorming:** I can help generate ideas for projects, business names, gifts, or creative endeavors.\n* **Math & Logic:** I can solve mathematical problems and work through logic puzzles.\n\nIs there something specific you would like help with today?\n```\n\nReasoning is on by default. The NIM returns the chain of thought in the `reasoning` field of the message, and `content` holds the final answer. Add `extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}}` for a direct answer.\n\n**Image**\n\n```python\nimport base64\n\ndef image_to_data_url(path: str) -> str:\n with open(path, \"rb\") as f:\n b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n return f\"data:image/jpeg;base64,{b64}\"\n\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"image_url\", \"image_url\": {\"url\": image_to_data_url(\"your_image.jpg\")}},\n {\"type\": \"text\", \"text\": \"Describe this image.\"},\n ],\n }\n ],\n max_tokens=16000,\n temperature=0.6,\n top_p=0.95,\n extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}},\n)\nprint(response.choices[0].message.content)\n```\n\nExpected output:\n\n```text\nThe image shows a capybara in a zoo enclosure. The capybara is eating green leafy vegetables on the ground. Its enclosure has a dirt floor and a large hollowed-out log behind it. There's a concrete wall visible, with trees and bushes beyond it. The capybara is facing right, with its head down as it eats. The scene is well-lit, suggesting it's daytime. The image captures a natural moment of the capybara's daily life in its zoo habitat.\n```\n\n**Video**\n\n```python\nimport base64\n\ndef video_to_data_url(path: str) -> str:\n with open(path, \"rb\") as f:\n b64 = base64.b64encode(f.read()).decode(\"utf-8\")\n return f\"data:video/mp4;base64,{b64}\"\n\nresponse = client.chat.completions.create(\n model=MODEL,\n messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"video_url\", \"video_url\": {\"url\": video_to_data_url(\"your_video.mp4\")}},\n {\"type\": \"text\", \"text\": \"Describe what happens in this video.\"},\n ],\n }\n ],\n max_tokens=16000,\n temperature=0.6,\n top_p=0.95,\n extra_body={\"chat_template_kwargs\": {\"enable_thinking\": False}},\n)\nprint(response.choices[0].message.content)\n```\n\nExpected output:\n\n```text\nThe video shows a rotating view of Earth from space, highlighting the illuminated areas of the planet against the dark backdrop of space. The continents and oceans are visible, with city lights indicating populated regions. The rotation provides a dynamic perspective of the Earth's surface, showcasing its curvature and the distribution of light across different landmasses.\n```\n\nFor video use cases, try both enabling and disabling thinking for best results. A public `https://` video URL also works in place of the base64 data URL.\n\n

\n\n**PDF input:** The API accepts images, not raw PDF files. Render each page to PNG (e.g. with `pymupdf`) and send it as a base64 `image_url` as in the Image example above.\n\n#### **Recommended Media Inputs**\n\n**Video**\n\nUse MP4 files of **1–2 minutes**. Every NIM profile samples at **2 FPS, up to 256 frames**, with Efficient Video Sampling (pruning rate 0.5), which covers roughly two minutes of footage. Longer clips are truncated to 256 frames.\n\n**Images**\n\nTested input configurations:\n\n| Input type | Resolution | Images per request |\n|---|---|---|\n| Simple image | 512×512 | 1 |\n| Document image | 1536×1024 | 1 |\n| Multi-document image | 1536×1024 | 32 |\n\nEach page image counts toward the prompt side of the context length.\n\n#### **Notes**\n\n1. **Video container and length:** MP4, 1–2 minutes. Other containers are untested.\n2. **Video decoding needs NVDEC access:** start the container with `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video`.\n3. **`max_tokens` vs context length:** `max_tokens` caps only the completion. Prompt plus completion must fit inside the profile's context length, and video prefill consumes a large share of it.\n\n## Training, Testing, and Evaluation Datasets\n\n### Training\n\n**Data Modality:** Text, images, and video \n**Foundation Training Data Size:** More than 20 trillion tokens\n**Image Training Data Size:** 1 Million to 1 Billion Images\n**Text Training Data Size:** More than 10 Trillion Tokens\n**Video Training Data Size:** 10,000 to 1 Million Hours\n**Foundation Data Collection Period:** 2013 to December 2025 \n**Data Collection Method:** Hybrid: automated, manually-collected, and synthetic \n**Labeling Method:** Hybrid: automated, manually-labeled, and synthetic\n\nThe foundation corpus contains curated and synthetic text covering English, 19 other spoken languages, and 43 programming languages. Sources include webpages, dialogue, articles, code, and other written materials across math, science, legal, finance, and general knowledge domains.\n\nThe post-training corpus includes reasoning, coding, tool use, instruction following, structured outputs, chat, safety, and long-context tasks, together with image and video understanding. Synthetic trajectories are filtered for structural correctness, repetition, and task validity.\n\nFor more information about the datasets used to train this model, please see the [Nemotron 3.5 Super VL EU Training Content Summary](https://assets.ngc.nvidia.com/products/api-catalog/nemotron-3.5-super-vl/Nemotron_3.5_Super_VL_EU_Training_Content_Summary.pdf).\n\n

Detailed dataset information

\n\n#### **Base Pre-Training Corpus (Nemotron 3 Foundation)**\n\nThe foundation of the model is trained on the Nemotron 3 corpus, comprising the following datasets from the [Nemotron Pretraining Datasets collection](https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets):\n\n| Dataset Collection | Token Counts | Description |\n| :--- | :--- | :--- |\n| **Nemotron-CC-v2** & **v2.1** | 9.1T | A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content. |\n| **Nemotron-CC-Code-v1** | 427.9B | High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations. |\n| **Nemotron-Pretraining-Code-v1** & **v2** & **v3** | 1.7T | Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data. |\n| **Nemotron-CC-Math-v1** | 133.3B | High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures. |\n| **Nemotron-Pretraining-Specialized-v1** & **v1.1** & **v1.2** & **Nemotron-Pretraining-SFT-v1** | 660.0B | Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding. |\n| **Nemotron-Pretraining-Legal-v1** | 4.3B | Synthetic datasets targeting the legal domain. |\n\n### Public Datasets\n\n| Dataset | Collection Period |\n| :---- | :---- |\n| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |\n| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |\n| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |\n| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |\n| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |\n| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |\n| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |\n| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |\n| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |\n| [Open Textbook Library - CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax - CC BY-SA subset](https://openstax.org/) | 4/23/2025 |\n| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |\n| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |\n| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |\n| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |\n| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |\n| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |\n| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |\n| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |\n| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |\n| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |\n| [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |\n| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |\n| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |\n| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |\n| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |\n| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |\n| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |\n| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |\n| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |\n| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |\n| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |\n| [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |\n| [FinQA](https://finqasite.github.io/) | Legacy Download |\n| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |\n| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |\n| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |\n| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |\n| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |\n| [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |\n| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |\n| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |\n| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |\n| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |\n| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |\n| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |\n| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |\n| [MultiverseMathHard](https://huggingface.co/datasets/Nexusflow/MultiverseMathHard) | 10/2/2025 |\n| [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | 10/2/2025 |\n| [WorkBench](https://github.com/olly-styles/WorkBench/tree/main/data/raw) | 10/2/2025 |\n| [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 10/2/2025 |\n| [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) | 10/2/2025 |\n| [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | 10/2/2025 |\n| [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) | 10/2/2025 |\n| [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified) | 10/2/2025 |\n| [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) | 10/2/2025 |\n| [simple-arithmetic-problems](https://huggingface.co/datasets/garrethlee/simple-arithmetic-problems) | 10/2/2025 |\n| [arithmetic](https://huggingface.co/datasets/EleutherAI/arithmetic) | 10/2/2025 |\n| [Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) | 10/2/2025 |\n| [FastChat](https://github.com/lm-sys/FastChat/blob/main/playground/data/dummy.json) | 10/2/2025 |\n| [News Commentary](https://opus.nlpl.eu/News-Commentary.php) | 10/2/2025 |\n| [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 10/2/2025 |\n| [finepdfs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) | 10/2/2025 |\n| [HotpotQA](https://huggingface.co/hotpot_qa/datasets) | 10/2/2025 |\n| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | 10/2/2025 |\n| [NLTK Words Lists](https://www.nltk.org/nltk_data/) | 10/2/2025 |\n\n### **Crawled and Scraped from Online Sources by NVIDIA**\n\nThe English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways used in the Nemotron-CC data pipeline. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.\n\nThe GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set.\n\n| Dataset | Modality | Dataset Size | Collection Period | Collecting Organisation |\n| :---- | :---- | :---- | :---- | :---- |\n| English Common Crawl | Text | 3.36T | 4/8/2025 | NVIDIA Advanced Deep Learning Research |\n| English Common Crawl 1.1 | Text | Not disclosed | 10/2/2025 | NVIDIA Advanced Deep Learning Research |\n| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 | NVIDIA Advanced Deep Learning Research |\n| GitHub Crawl | Text | 747.4B | 4/29/2025 | NVIDIA Advanced Deep Learning Research |\n| GitHub Crawl 1.1 | Text | 172.7B | 9/30/2025 | NVIDIA Advanced Deep Learning Research |\n| [Sangraha](https://huggingface.co/datasets/ai4bharat/sangraha) | Text | 109,263,115 indexed source sequences: 77,231,480 verified and 32,031,635 unverified | Undisclosed | NVIDIA Advanced Deep Learning Research |\n| LITA Chunked Video Labels, Custom No-Thinking Dense Captions | Text (video labels) | 151,349 records | Undisclosed | NVIDIA Advanced Deep Learning Research|\n| LITA Chunked Video Labels, Cleaned v2 | Text (video labels) | 150,434 records | Undisclosed | NVIDIA Advanced Deep Learning Research |\n\n## Private Non-publicly Accessible Datasets of Third Parties\n\n| Dataset | Model(s) used |\n|---------|---------------|\n| Global Regulation | Unknown |\n| TAUS Translation Memory | Unknown |\n| Scale HLE | Unknown |\n| HackerRank Coding | Unknown |\n| RL data for Search | Gemini 3; GPT-5 |\n| Mercor SWE-AgentsV1 | Undisclosed |\n| KeenSight Batch 1, Relabeled Subset | Qwen3.5-122B-A10B |\n| KeenSight Batch 1, Remaining Subset | Qwen3.5-122B-A10B |\n| Camio Multilingual OCR and Document Layout | Undisclosed |\n| Chinese Visual Question Answering, Relabeled Subset | Qwen3.5-122B-A10B |\n| Camio Chinese OCR, Cleaned Variant | Qwen3.5-122B-A10B |\n| Camio English OCR, Cleaned Variant | Qwen3.5-122B-A10B |\n| KeenSight Batch 2, Remaining Subset | Qwen3.5-122B-A10B |\n| KeenSight Batch 1, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B |\n| Chinese Visual Question Answering, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B |\n| Camio Chinese Question Answering, Verified-Correct Subset | Qwen3.5 |\n| KeenSight Chinese OCR, Filtered Subset | DeepSeek-OCR |\n| Camio Synthetic Trading-Graph Question Answering | Undisclosed |\n| Camio Synthetic Menu and Receipt Question Answering | Qwen3.5-122B-A10B |\n| Camio Chinese OCR, Formatted Variant | Qwen3.5-122B-A10B |\n| Camio Synthetic Time-Series Question Answering | Undisclosed |\n| Camio English OCR, Formatted Variant | Qwen3.5-122B-A10B |\n| Lean Formal Proofs — RL | Undisclosed |\n| Document-Grounded Finance Reasoning, Patch Tasks — RL | Undisclosed |\n| Math CoT — SFT | Kimi K3 |\n| Chat — SFT | Undisclosed |\n| Chat, Reasoning Off — SFT | Undisclosed |\n| Conversational Tool-Use Simulation — RL | Undisclosed |\n| Math CoT, Low Variant — SFT | Kimi K3 |\n| Document-Grounded Finance Reasoning — SFT | Undisclosed |\n| Safety Preference v0.3 — RL | Undisclosed |\n| Response-Length Instruction Following, Reasoning Off — SFT | Undisclosed |\n| Math Python TIR, Low Variant — SFT | Kimi K3 |\n| Math Python TIR — SFT | Kimi K3 |\n| General-Purpose Tool Calling — SFT | Qwen3.5-397B-A17B; Gemma-4-31B-it |\n| Knowledge Agent — SFT | Undisclosed |\n| DataFunnel arXiv Science4 v1.0 — RL | DeepSeek-V4-Flash (problem extraction); GLM-5.2 (refinement); Kimi K3 and K3 V5 (verification and filtering); Super 3.5 r14 (difficulty profiling); Qwen3-235B-A22B-Instruct-2507-FP8 (reward judging) |\n| Structured Outputs v3 — RL | gpt-oss-120b |\n| Response-Length Instruction Following — SFT | Undisclosed |\n| Filtered CUDA Coding — SFT | Undisclosed |\n| Reasoning Gym — RL | Undisclosed |\n| Competitive Coding Challenges v2 — SFT | GLM-5.2 |\n| Multi-Turn Instruction Following, Reasoning Off — SFT | Undisclosed |\n| Multi-Turn Instruction Following — SFT | Undisclosed |\n| Instruction Following, Filtered RLHF Prompts — SFT | Undisclosed |\n| Citation Formatting — RL | gpt-oss-120b |\n| Long-Context General Data — SFT | Undisclosed |\n| Free-Form Formatting — RL | gpt-oss-120b |\n| Structured Outputs v4 — RL | Undisclosed |\n| Tool-Call Schema — RL | No model used for RL conversion; source-generation models undisclosed |\n| Competitive Coding, OCR Python — SFT | DeepSeek-V4-Pro |\n| Lean Formal Proofs — SFT | gpt-oss-120b; Goedel-Prover-V2-32B; Qwen2.5-32B-Instruct |\n| Citation Instruction Following — RL | Undisclosed |\n| Search Graph Walks — SFT | MiniMax-M2 |\n| General-Purpose Tool Calling, Reasoning Off — SFT | Undisclosed |\n| Knowledge Agent Tau3, Reasoning Off — SFT | Undisclosed |\n| ARC-AGI Puzzle Reasoning — SFT | Kimi-K2.5; Qwen3-235B-A22B-Thinking-2507; DeepSeek-V3.2; Qwen3-Next-80B-A3B-Thinking; GLM-4.5-Air-FP8; gpt-oss-120b; GLM-4.7-FP8; Ring-1T; Ministral-3-14B-Reasoning-2512 |\n| Calendar — RL | Undisclosed |\n| ACEProof — SFT | Undisclosed |\n| Long Translation — SFT | Undisclosed |\n| Agentless Software Engineering — SFT | Undisclosed |\n| Long-Context PDF Translation v1.0 — SFT | DeepSeek-V4-Flash (translation generation); GPT-5.6 Sol (filtering and judging) |\n\n## Private Non-publicly Accessible Datasets by NVIDIA\n\n| Dataset | Model(s) used |\n|---------|---------------|\n| Simple Minesweeper | Undisclosed |\n| Simple Sudoku | Undisclosed |\n| Multitool Typewriter Hard | Undisclosed |\n| Machine Translation of News Commentary and TAUS Translation Memory | Undisclosed |\n| Machine Translation of STEM - | [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) |\n| Competitive Coding RL data from Nemotron Cascade | Undisclosed |\n| Long context RL | Undisclosed |\n| Single-step SWE RL for patch generation | Undisclosed |\n| OpenHands SWE | Undisclosed |\n\n## NVIDIA-Sourced Synthetic Datasets (Pre-Training)\n\n| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |\n| :---- | :---- | :---- | :---- | :---- |\n| Nemotron-Pretraining-Fact-Seeking | Text | 35.0B | [FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) | [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) |\n| Nemotron-Pretraining-Legal | Text | 4.3B | CommonPile (caselaw_access_project_filtered); California Code of Regulations; Judicial Ethics Opinions; [GLOBALCIT](https://globalcit.eu/); [CUAD](https://www.atticusprojectai.org/cuad); Nemotron Personas; [ToSDR Terms of Service Corpus](https://www.kaggle.com/datasets/sonu1607/tosdr-terms-of-service-corpus); CodeHima/TOS_Dataset; [ContractNLI](https://stanfordnlp.github.io/contract-nli/); CaseHOLD; Code of Federal Regulations; [Canadian Case Law](https://huggingface.co/datasets/a2aj/canadian-case-law) (subsets that allow commercial use) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |\n| Nemotron-Pretraining-Formal-Logic | Text | 128M | [Nemotron Personas](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |\n| Nemotron-Pretraining-Economics | Text | 73.4M | - | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |\n| Nemotron-Pretraining-Multiple-Choice | Text | 1.6B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |\n| Nemotron-Pretraining-Code-Concepts | Text | 7.3B | - | [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Nemotron-Pretraining-Unconditional-Algorithmic | Text | 196.5M | - | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |\n| More Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 1.1B | train splits of acp_bench; ai2_arc; babi; gsm8k; hendrycks_math; IFEval; MedText; mediqa_qa; mlqa; MMLU-Pro; mmlu-pro-plus; MMLU-ProX; nq_open; tinyGSM8k; truthful_qa; truthfulqa-multi; MATH-lighteval; mmlu; awesome-chatgpt-prompts; super_glue | [DeepSeek v3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |\n| Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 6.7B | train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP | [DeepSeek v3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |\n| Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 40B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |\n| Synthetic Moral Stories and Social Chemistry from Qwen3-235B-A22B-Thinking-2507 and Mixtral-8x22B-v0.1 | Text | 15.2M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |\n| Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |\n| Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 83.6M | [OpenStax - CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |\n| Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 9.7M | [OpenStax - CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |\n| Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B | Text | 175M | [OpenStax - CC BY-SA subset](https://openstax.org/); [GSM8K](https://github.com/openai/grade-school-math); [Open Textbook Library - CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |\n| [Nemotron-PrismMath](https://huggingface.co/datasets/nvidia/Nemotron-PrismMath) | Text | 4.6B | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified); [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | [Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct); [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |\n| Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |\n| Synthetic Rephrased [Math Data from Common Crawl](https://huggingface.co/datasets/nvidia/Nemotron-MIND) from phi-4 | Text | 73B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |\n| Synthetic Math Data from Common Crawl 4plus | Text | 52.3B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |\n| Synthetic Math Data from Common Crawl 3 | Text | 80.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |\n| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |\n| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k) | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B); [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B); [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |\n| Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |\n| Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | Undisclosed | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |\n| Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 415.8B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) |\n| Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text | Undisclosed | - | [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) |\n| Synthetic Common Crawl Code from phi-4 | Text | 427.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |\n| Synthetic Scientific Coding from Qwen3-235B-A22B | Text | 1.2B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |\n| Tool Calling Data | Text | 26.2B | - | [Qwen3-235B-A22B-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic Essential-Web from QwQ-32B | Text | 28.1B | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |\n| Translated Synthetic Crawl | Text | 389.9B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Translated Synthetic Wikipedia | Text | 7.9B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |\n| Synthetic Search STEM OPENQ from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | - | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | - | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528 | Text | Undisclosed | - | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B); [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503); [Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506); [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k); [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k); [Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B | Text | Undisclosed | [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M); [arena-human-preference-140k](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it); [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it); [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1); [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct); [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2); [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct); [Phi-3-small-8k-instruct](https://huggingface.co/microsoft/Phi-3-small-8k-instruct); [Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |\n| Synthetic Code from Qwen3-32B | Text | Undisclosed | English Common Crawl; English Common Crawl 1.1 | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) |\n| Synthetic OpenCodeReasoning from DeepSeek-R1 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |\n| Synthetic OpenCodeReasoning from DeepSeek-R1-0528 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic HackerRank Coding from DeepSeek-R1-0528 | Text | Undisclosed | HackerRank Coding Dataset | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic LIMO from DeepSeek-R1-0528 | Text | Undisclosed | [LIMO](https://huggingface.co/datasets/GAIR/LIMO) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic SCP from DeepSeek-R1-0528 | Text | Undisclosed | [SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Stack Exchange from DeepSeek-R1-0528 | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |\n| Synthetic Stack Exchange from gpt-oss-120b | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |\n| Synthetic Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Wikipedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |\n| Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |\n| Synthetic Essential-Web from gpt-oss-120b | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4 | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/); [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [phi-4](https://huggingface.co/microsoft/phi-4) |\n| Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528 | Text | Undisclosed | [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K); [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2); [TACO](https://huggingface.co/datasets/BAAI/TACO); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning); [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | - | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |\n| Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |\n| Synthetic KernelBook from DeepSeek-R1-0528 | Text | Undisclosed | [KernelBook](https://huggingface.co/datasets/GPUMODE/KernelBook) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic Scale HLE from gpt-oss-120b | Text | Undisclosed | Scale HLE | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic CDQuestions from gpt-oss-120b | Text | Undisclosed | [CDQuestions](https://cdquestions.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |\n| Synthetic Vedantu from gpt-oss-120b | Text | Undisclosed | [Vedantu](https://www.vedantu.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |\n| Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | - | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | - | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic MCQ10 from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |\n| Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | - | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |\n\n## NVIDIA-Sourced Synthetic Datasets (Post-Training)\n\n| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |\n| :---- | :---- | :---- | :---- | :---- |\n| Synthetic Single-Step Tool Use — RL | Text | Approximately 1,433,203 samples | Handwritten prompts; no external seed data | DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B |\n| Synthetic Sparse Tables with Mixed Prompts | Text and images | 100,000 records | Generated in-house; no external seed data | None; generated without an SDG model |\n| Synthetic Single-Step Tool Use — RL | Text | Approximately 1,433,203 samples | Handwritten prompts; no external seed data | DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B |\n| Synthetic Sparse Tables with Mixed Prompts | Text and images | 100,000 records | Generated in-house; no external seed data | None; generated without an SDG model |\n| Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Hermes Agent Reasoning Traces | Text | Undisclosed | [lambda/hermes-agent-reasoning-traces] | [hermes-agent-generator] |\n| Synthetic Competitive Coding from DeepSeek-V4-Pro | Text | Undisclosed | [NVCompetitiveCodingV1] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset] | [deepseek-ai/DeepSeek-V4-Pro] |\n| Synthetic Competitive MATH CoT and TIR from Nemotron 5.5 | Text | Undisclosed | [Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [Nemotron 5.5] |\n| Vendor Terminal Bench-like Tasks from Mercor | Text | Undisclosed | [Terminal bench like tasks curated by the vendor] | [Undisclosed - purchased dataset] |\n| Turing Math Data Pack | Text | Undisclosed | [Turing Math Data Pack dataset] | [Undisclosed - purchased dataset] |\n| Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5 | Text | Undisclosed | [DocQA-RL-1.6K]; [DAPO-Math-17k] | [GPT-5.5] |\n| Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K | Text | Undisclosed | [DocQA-RL-1.6K] | Undisclosed |\n| Synthetic Competitive Coding Gym Tasks | Text | Undisclosed | [NVCompetitiveCodingV1.1] | Undisclosed |\n| Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3 | Text | Undisclosed | [SEC filings from sec.gov] | [GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct] |\n| Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Nemotron-RL-agent-structured-outputs-v1] | [Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1 | Text | Undisclosed | [Long-context SFT data] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1] |\n| Synthetic Science RL Data Blend from Qwen2.5-32B | Text | Undisclosed | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [Qwen2.5-32B] |\n| Synthetic Abstention Data from Nemotron Super v3 | Text | Undisclosed | [Go abstention Dataset] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Chemistry Data from Nemotron Super v3 | Text | Undisclosed | [ChemData 700K] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Tool Call Schema for RL | Text | 469,983 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] |\n| Synthetic Freeform Text Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] |\n| Synthetic Citation Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] |\n| Droid Harness Pivot Vendor Data | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed |\n| Synthetic HotpotQA Training Data from Qwen3-235B | Text | Undisclosed | [HotpotQA] | [Qwen3-235B] |\n| Synthetic Natural Language Math Proofs from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [Nemotron 5.5] |\n| Synthetic Stack Overflow OpenQ | Text | Undisclosed | [Pile-FreeLaw] | Undisclosed |\n| Chemistry Ether0 Vendor Data | Text | Undisclosed | [Chemistry ether0 vendor data] | Undisclosed |\n| Synthetic Litmus-Bench Chemistry from ChEMBL | Text | Undisclosed | [ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit] | Undisclosed |\n| Synthetic ZINC Chemistry from Nemotron Super v3 | Text | Undisclosed | [ZINC] | [Nemotron Super v3] |\n| ARC-AGI Gym Environment | Text | Undisclosed | [ARC-AGI-2] | [ARC-AGI-2] |\n| Synthetic Agentic Search Tool-Use from DeepSeek-V3.2 | Text | Undisclosed | [Mercor Data] | [DeepSeek-V3.2] |\n| Synthetic Text-To-SQL | Text | 96,564 | [In-house Text-to-SQL data] | [gpt-oss-120b] |\n| Dialog Memory Vendor Data | Text | Undisclosed | [Patronus external vendor agreement] | Undisclosed |\n| Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct | Text | Undisclosed | [In-house indirect prompt injection data] | [nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct] |\n| Synthetic Malicious Code and Agentic Security | Text | Undisclosed | [In-house malicious-code / agentic-security data] | Undisclosed |\n| Synthetic Single-Step SWE Patch Selection | Text | Undisclosed | [SWE-Gym Dataset]; [SWE Bench Verified Benchmark] | [ground truth and task checks] |\n| Synthetic Natural Language Math Final Answers from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [nemotron 5.5] |\n| Synthetic Simple Math Prompts for Token Efficiency | Text | Undisclosed | [In-house simple math prompts] | Undisclosed |\n| Synthetic Abstention Data from Nemotron Super v3 (CRAG) | Text | Undisclosed | [CRAG] | [nvidia/nvidia/nemotron-3-super-v3] |\n| Synthetic Agentless SWE | Text | 242,536 | [SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench] | [openai/gpt-oss-120b] |\n| Synthetic Agentless SWE from DeepSeek-R1-0528 | Text | 209,976 | [SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith] | [deepseek-ai/DeepSeek-R1-0528] |\n| Synthetic Agentic CUDA Traces from GLM-4.7 | Text | 2,276 | [Internal CUDA task data] | [GLM-4.7] |\n| Synthetic Math Proofs from DeepSeek-V3.2-Speciale | Text | 820,772 | [Nemotron-Math-Proofs-v1] | [SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation] |\n| Synthetic Multilingual SFT from DeepSeek-V3 | Text | 1,245,284 | [Nano v3 SFT data] | [DeepSeek-V3] |\n| Synthetic Agentic Code from gpt-oss-120b | Text | 109,086 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1] | [openai/gpt-oss-120b] |\n| Synthetic Agentic CLI and Web Skills from gpt-oss-120b | Text | 27,418 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic Agentic Coding from gpt-oss-120b | Text | 160,531 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic OpenCode Agentic Tasks from gpt-oss-120b | Text | 614,773 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] |\n| Synthetic SWE Unverified | Text | Undisclosed | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash] |\n| Synthetic ARC-AGI Ultra Data | Text | 192,016 | [ARC-AGI-2]; [arc dataset collection] | [ARC-AGI-2] |\n| Synthetic LiveCodeBench TIR from DeepSeek-R1-0528 | Text | 1,283,398 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] |\n| Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B | Text | 1,233,247 | [Verilog/SystemVerilog seed code] | [SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet] |\n| Synthetic Aider Python Tasks from DeepSeek-R1-0528 | Text | 236,099 | [Exercism (GitHub Python)] | [Deepseek R1 0528] |\n| Synthetic Chat Reasoning-Off Data from GLM-5 | Text | 646,738 | [lmarena-ai/repochat-arena-preference-4k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] |\n| Synthetic Chat Reasoning-On Data from GLM-5 | Text | 644,286 | [lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] |\n| Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1 | Text | 132,067 | [Safety SFT Data: Ultra] | [nvidia/Riva-Translate-4B-Instruct-v1.1] |\n| Synthetic Science Reasoning Effort Medium | Text | 502,722 | [science-reasoning-effort-medium-v0] | Undisclosed |\n| Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b | Text | 12,455 | [Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2] | [gpt-oss-120b] |\n| Synthetic Terminal Bench Data from OpenReasoningv2 | Text | Undisclosed | [OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K] | [OpenReasoningv2] |\n| Synthetic Tulu Instruction Following from DeepSeek-R1-0528 | Text | 105,361 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] |\n| Synthetic Instruction Following from gpt-oss-120b | Text | 151,988 | [IFEval]; [IFEvalG] | [gpt-oss-120b] |\n| Synthetic Instruction Following for RL | Text | Undisclosed | [WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507 | Text | 25,992 | [Hand-written prompts] | [Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Terminus Ultra Agentic Reasoning Blend | Text | 96,881 | [ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith] | [DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8] |\n| Synthetic STEM from Qwen3-235B-A22B-Thinking-2507 | Text | 1,174,694 | [IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning] | [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b | Text | Undisclosed | [arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD] | [Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b] |\n| Translation Data from TAUS | Text | 1,618,055 | [TAUS proprietary dataset] | Undisclosed |\n| Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B | Text | 860,469 | [Nemotron-Math-Proofs-v1] | [Goedel-Prover-V2-32B] |\n| Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b | Text | 1,201,815 | [Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange] | [gpt-oss-120b] |\n| Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct | Text | Undisclosed | [Nano-V3 SFT Data (without tool call)] | [Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507] |\n| Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage) | Text | Undisclosed | [Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces | [DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B] |\n| Synthetic Search Graph Walk | Text | 6,977 | [Wikidata / Wikipedia KnowledgeBase] | [MiniMaxAI/MiniMax-M2] |\n| Synthetic Agentic Diverse Domains | Text | 281,537 | [Handwritten prompts (synthetic; no external seed data used)] | [SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B] |\n| Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale | Text | 1,900,553 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2-Speciale] |\n| Synthetic Nemotron Math TIR from DeepSeek-V3.2 | Text | 1,789,258 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2] |\n| Synthetic NemoCascade OCR Distillation from gpt-oss-120b | Text | 682,864 | [Nemotron-X training datasets] | [gpt-oss-120b] |\n| Synthetic CUDA 100k | Text | 93,086 | [KernelBook]; [HuggingFace Transformers]; [FlashInfer] | [gpt-oss-120b]; [DeepSeek-R1-0528] |\n| Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2 | Text | 30,358 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science HLE with Python from GPT-OSS and Kimi-K2 | Text | 85,184 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Search and Python from GPT-OSS and Kimi-K2 | Text | 6,179 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Search from GPT-OSS and Kimi-K2 | Text | 32,554 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507 | Text | 326,700 | [SEC filings] | [GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507] |\n| Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2 | Text | 532,942 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2 | Text | 131,045 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2 | Text | 2,085,600 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] |\n| Synthetic Text-To-SQL from gpt-oss-120b | Text | Undisclosed | [In-house Text-to-SQL data] | [gpt-oss-120b] |\n| Synthetic Tool Call Schema for RL (extended) | Text | 707,967 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] |\n| Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b | Text | 44,091 | [Safety SFT Data] | [google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b] |\n| Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA] | [DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1] |\n| Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking | Text | Undisclosed | [ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA] | [Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking] |\n| Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | NVIDIA Internal | [gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking | Text | Undisclosed | [DocFinQA]; [SWE-smith] | [Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking] |\n| Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym] | [Qwen3-Coder-480B-A35B-Instruct] |\n| Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [Qwen3-Coder-480B-A35B-Instruct] |\n| Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528 | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [DeepSeek-R1-0528] |\n| Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] |\n| Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B | Text | Undisclosed | [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-8B] |\n| Vendor Terminal Bench-like Tasks (Droid) | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed |\n\n## Language Distribution in Post-Training\n\nFor our post-training recipe, we focused on the following languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese. Those languages were represented in the form of multilingual reasoning and translation tasks.\n\n

\n\n### Additional Training Data\n\n| Data | Composition and Purpose |\n|:---|:---|\n| Indic continued pre-training | 100B tokens: 10B selected Indic tokens and 90B tokens from the preceding blend. |\n| Software-engineering mid-training | 67B tokens: whole repositories (20%), issue–pull-request pairs (41%), commit diffs (3%), build and CI logs (1%), agent traces (10%), pre-training replay (20%), and long-context data (5%). Sources are decontaminated against SWE-bench and Terminal-Bench repositories. |\n| Long-context pre-training | A 35B-token extension to 1M context, followed by a 9B-token long-context recovery stage. |\n| Vision-language data | Image and video data from the Nemotron 3 Nano Omni training corpus, including visual question answering, charts, tables, OCR, multi-page documents, visual grounding, and video understanding. |\n\n| Post-Training Data | Composition and Purpose |\n|:---|:---|\n| Additional synthetic vision data | Chart understanding, grounded perception, multimodal reasoning, and visual tool-use trajectories, including data generated with Qwen3.5 and Kimi models. |\n| Reasoning trajectories | Mathematics and STEM responses generated with Kimi K3; mathematical proofs generated with DeepSeek V4 Pro; competitive-programming trajectories generated with GLM-5.2. |\n| Chat and safety | Refreshed chat responses from Qwen, Kimi, and Gemma models, with additional safety data. |\n| Long-context supervised data | 512K-context data, synthetic graph reasoning and transduction tasks, long-horizon search and coding trajectories, and documents containing up to hundreds of pages. |\n| Long-document translation | Chunked translations of PG19 and FinePDFs-Edu documents generated with DeepSeek V4 Flash. |\n\n## Testing Datasets\n\n**Data Collection Method:** Hybrid: automated, manually-collected, and synthetic \n**Labeling Method:** Hybrid: automated, manually-labeled, and synthetic \n**Properties:** Standard benchmarks and task-specific test suites covering reasoning, coding, tool use, instruction following, long context, and visual understanding.\n\n## Evaluation Datasets\n\n**Data Collection Method:** Hybrid: automated, manually-collected, and synthetic \n**Labeling Method:** Hybrid: automated, manually-labeled, and synthetic \n**Properties:** Standard benchmarks and task-specific test suites covering reasoning, coding, tool use, instruction following, long context, and visual understanding.\n\n## Inference\n\n* **Acceleration Engine:** vLLM (NVIDIA NIM)\n* **Test Hardware:**\n * NVIDIA Ampere\n - 2x A100-80GB\n * NVIDIA Blackwell\n - 1-2x B200, 1-2x GB200\n - 1-2x B300, 1-2x GB300\n - 2x RTX Pro 6000 Blackwell Server Edition\n - DGX Station\n - DGX Spark\n * NVIDIA Hopper\n - 2-8x H100\n - 1-4x H200\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nWe advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: [Safety](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/blob/main/safety.md) and [Explainability](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/blob/main/explainability.md) subcards. For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/blob/main/bias.md), and [Privacy](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/blob/main/privacy.md) subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## **Get Help**\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","Nemotron-3.5-Super-VL-120B-A12B",["D",1792048433020],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nim/nvidia/models/nemotron-3.5-super-vl-120b-a12b","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_276":277,"_278":279,"_280":281,"_282":35,"_283":284},"isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| :---- | :---- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |\n| Bias Metric (If Measured): | [BBQ Accuracy Scores in Ambiguous Contexts](https://github.com/nyu-mll/BBQ/) |\n| Which characteristic (feature) show(s) the greatest difference in performance?: | The model shows high variance in the characteristics when it is used with a high temperature. |\n| Measures taken to mitigate against unwanted bias: | Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) employed to calibrate the model’s reasoning capabilities to maintain logical consistency and appropriate complexity when interacting with or interpreting data from diverse age demographics. |\n| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | The training datasets contain a large amount of synthetic data generated by LLMs. We manually curated prompts. |\n| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | [BBQ](https://github.com/nyu-mll/BBQ/) |\n| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as web-scraped finance reasoning data derived from SEC EDGAR filings, science and math problem datasets, OpenResearcher/source-document datasets, Common Crawl, CC-News, Wikimedia, long-context document datasets, and image datasets including BLIP3, PixMo, COCO-derived collections, and document, chart and page-image datasets, do not collectively or exhaustively represent all demographic groups (and proportionally therein). \nFor instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in approximately 97% to 99.9% of finance reasoning samples and in over 85% of samples across the broader assessed text datasets, and automated person-presence screening did not detect a person in approximately 86% of the images analyzed across 40 image datasets (and in over 98% of images in 18 document-, chart- and filing-page-focused datasets). In the subset where such terms or people are present, these datasets contain representational skews; for example, in text, ethnicity mentions are often dominated by Middle Eastern contexts (found in finance documents) or \"White,\" \"Two or more,\" and \"Black or African American\" as the most frequent ethnic identifiers, while references categorized as male-only significantly outnumber those categorized as female-only. In image datasets, where person-present signals were identified, these datasets contain uneven representation splits across the measured visual taxonomies: apparent age was predominantly adult, followed by elder; gender presentation skewed male; and automated apparent-ethnicity candidates were led by White and Asian. To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, task-specific fairness evaluation, red-teaming, fine-tuning with demographically balanced datasets, and mitigation strategies such as counterfactual data augmentation to align with the desired model behavior. This evaluation used a 1,000-3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy, and includes outputs from uncalibrated embedders; as such, certain limitations may exist in the reliability |\n| Unwanted Bias Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |","explainability","| Field | Response |\n| :---- | :---- |\n| Intended Task/Domain: | Text generation, reasoning, and chat |\n| Model Type: | Text-to-text Mamba2-Transformer Hybrid |\n| Intended Users: | Generative AI creators working with conversational AI models and image content. |\n| Output: | Text |\n| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | We used a Gemma-3 4B-based filtering model fine-tuned on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) to ensure the quality of synthetic data. |\n| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Disability Status, Gender Identity, Nationality, Physical Appearance, Ethnicity, Socioeconomic Status, Sexual Orientation, Religion |\n| Technical Limitations & Mitigation: | This model performs particularly well in instruction following regimes, as such may be strongly influenced by untrusted inputs and should be paired with appropriate guardrails and data filtering to better align use-case behaviors when exposed to such data. |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |\n| Performance Metrics: | Accuracy, Throughput, and User-side throughput |\n| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place. The model may generate answers that may be inaccurate, omit key information, include irrelevant or redundant text, or produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model may exhibit self-anthropomorphism (e.g., displaying human-like characteristics in dialogue, such as expressing preferences and emotions). |\n| Licensing: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) (OpenMDW-1.1). |","privacy","| Privacy Information |\n| :--- |\n| Nemotron 3.5 Super VL was trained on large-scale publicly available data that may contain images, audio-video, and text relating to people. NVIDIA collected and used this data in compliance with applicable data protection and privacy laws. This model was not designed to derive insights or otherwise learn from any personal data contained in the datasets. |\n| NVIDIA uses a combination of filters, data minimization techniques, and other guardrails to help prevent personal data from being recited by our models. We employ automated tools and data processing techniques during pre-training or training to identify and filter certain categories of personal data. |\n| Please review NVIDIA's [Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) for more information. |","safetyAndSecurity","| Field | Response |\n| :---- | :---- |\n| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |\n| Describe the life critical impact (if present). | Not Applicable |\n| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training. |\n| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |\n| Use Case Restrictions: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) (OpenMDW-1.1).|\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |\n| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |","featureLabels",[267,268],"displayLabels",[256,257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_220":265,"_259":265,"_260":266,"_262":263},{"_220":258,"_259":258,"_260":261,"_262":263},"NSPECT-W16P-W1SZ","value","filter",{"_264":263,"_259":258},"category","label","field","ingested",{"_264":263,"_259":265},{"_220":274,"_259":274,"_260":275,"_262":272},{"_220":269,"_259":270,"_260":271,"_262":272},"NVIDIA NIM","nimmcro_nvidia_nim",{"_264":273,"_259":270},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_264":263,"_259":274},"governingTerms","

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GOVERNING DOWNLOAD TERMS: Use of the NIM container is governed by the NVIDIA Software License Agreement, Product-Specific Terms for NVIDIA AI Products.

Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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GOVERNING HOSTING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service. Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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