Nemotron-RL-knowledge-mcqa

## Dataset Description:

The Nemotron-RL-knowledge-mcqa is a multi-domain synthetic multiple-choice question-answering (MCQA) dataset containing knowledge based questions. It combines and refines subsets of the [OpenScienceReasoning-2] (https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset and other unstructured sources such as books and articles.The dataset was created using [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B), [Qwen3-235B-A22B-Instruct-2507] (https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507), and [DeepSeek-R1-0528](​​https://huggingface.co/deepseek-ai/DeepSeek-R1-0528). Each sample consists of a question with multiple answer options and one correct answer. The dataset spans a broad range of domains, including physics, biology, chemistry, mathematics, computer science, engineering, humanities, law, and others.

This dataset is released as part of NVIDIA [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym), a framework for building reinforcement learning environments to train large language models. NeMo Gym contains a growing collection of training environments and datasets to enable Reinforcement Learning from Verifiable Reward (RLVR).

NeMo Gym is an open-source library within the [NVIDIA NeMo framework](https://github.com/NVIDIA-NeMo/), NVIDIA's GPU accelerated, end-to-end training framework for large language models (LLMs), multi-modal models and speech models.

This dataset is part of the [Nemo Gym Collection](https://huggingface.co/collections/nvidia/nemo-gym).

This dataset is ready for commercial use.

## Dataset Owner(s):
NVIDIA Corporation

## Dataset Creation Date:
October 20, 2025

## License/Terms of Use:
CC BY 4.0

## Intended Usage:
To be used with [NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym) for post-training LLMs.

## Dataset Characterization
Data Collection Method<br>
* [Synthetic] <br>

Labeling Method<br>
* [Synthetic] <br>

## Dataset Format
Text Only, Compatible with [NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)

## Dataset Quantification
Number of records: 685,573 tuples of (question, answer)
Features per record: 6
Total Data Storage: 757 MB

## Reference(s):
[NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)

## Ethical Considerations:
NVIDIA 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.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

数据集描述：

Nemotron-RL-knowledge-mcqa是一款多领域合成式多项选择问答（Multiple-Choice Question Answering，MCQA）数据集，涵盖基于知识的问答题目。本数据集整合并精炼了[OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2)数据集的子集，以及书籍、文章等非结构化数据源的相关内容。该数据集由[Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)、[Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)及[DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)构建而成。每条样本均包含一道带有多个候选答案的问题及一个正确答案。数据集覆盖物理、生物、化学、数学、计算机科学、工程学、人文社科、法学等众多领域。

本数据集作为NVIDIA [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)的组成部分发布，该框架专为构建用于训练大语言模型（Large Language Model，LLM）的强化学习环境而设计。NeMo Gym收录了日益丰富的训练环境与数据集，以支持基于可验证奖励的强化学习（Reinforcement Learning from Verifiable Reward，RLVR）。

NeMo Gym是[NVIDIA NeMo框架](https://github.com/NVIDIA-NeMo/)下的开源库，该框架是NVIDIA推出的GPU加速型端到端训练框架，可用于大语言模型（LLM）、多模态模型与语音模型的训练。

本数据集隶属于[Nemo Gym数据集合集](https://huggingface.co/collections/nvidia/nemo-gym)。

本数据集可用于商业场景。

数据集归属方：NVIDIA公司

数据集创建日期：2025年10月20日

许可/使用条款：CC BY 4.0协议

预期用途：配合[NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)用于大语言模型的后训练流程。

数据集特征
数据收集方法：* [合成生成]

标注方法：* [合成生成]

数据集格式：仅文本格式，兼容[NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)

数据集量化统计
记录数量：685,573条（问题、答案）元组
每条记录的特征数：6
总数据存储量：757 MB

参考资料：[NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)

伦理考量：
NVIDIA认为可信人工智能是一项共同责任，我们已建立相关政策与实践规范，以支撑各类AI应用的开发。开发者在按照服务条款下载或使用本数据集时，应与其内部模型团队协同配合，确保该数据集符合对应行业与应用场景的要求，并应对潜在的产品误用风险。
如需报告模型质量、安全漏洞、相关风险或NVIDIA人工智能相关问题，请[点击此处](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)提交。