Nemotron-RL-knowledge-web_search-mcqa

## Dataset Description:

The Nemotron-RL-knowledge-web_search-mcqa is a multi-domain synthetic dataset designed to improve science and general reasoning in large language models (LLMs). It is a filtered subset of the [OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset and contains multiple-choice question–answer pairs spanning diverse domains: physics, biology, mathematics, humanities, computer science, engineering, chemistry, 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:
June 20th, 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
Record Count: 2930 tuples of (question, answer)

## 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-web_search-mcqa 是一款多领域合成数据集，旨在提升大语言模型（Large Language Model，LLM）的科学推理与通用推理能力。该数据集是 [OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) 数据集的过滤子集，包含覆盖多领域的多项选择题问答对，涉及物理学、生物学、数学、人文科学、计算机科学、工程学、化学等多个领域。

本数据集作为英伟达（NVIDIA）推出的 [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym) 的一部分发布，该框架用于构建用于训练大语言模型的强化学习环境。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加速式端到端训练框架，可用于大语言模型、多模态模型与语音模型的训练。

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

本数据集可用于商业用途。

## 数据集所有者：
英伟达公司（NVIDIA Corporation）

## 数据集创建日期：
2025年6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阶段。

## 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：2930条（问题、答案）二元组

## 参考文献：
[NeMo-Gym](https://github.com/NVIDIA-NeMo/Gym)

## 伦理考量：
英伟达（NVIDIA）认为，可信人工智能是一项共同责任，我们已制定相关政策与实践规范，以支持各类人工智能应用的开发。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作，确保所涉模型符合相关行业与应用场景的要求，并应对可能出现的产品误用问题。若需反馈模型质量、风险、安全漏洞或NVIDIA人工智能相关问题，请访问 [此处](https://www.nvidia.com/en-us/support/submit-security-vulnerability/) 提交。