ReaRAG-20k is a reasoning-focused dataset designed for training the ReaRAG model. It contains approximately 20,000 multi-turn retrieval examples constructed from the QA datasets such as HotpotQA, MuSiQue, and Natural Questions (NQ).
Each instance follows a conversational format supporting reasoning and retrieval steps:
{
"messages": [{"role": "user", "content": "..."},
{"role": "assistant", "reasoning": "..."},
{"role": "observation", "content": "..."}, ...]
}
During sft, the loss is computed only on messages that contain the reasoning key, rather than the content key.
If you use this dataset in your research or projects, please consider citing our work:
@article{lee2025rearag,
title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
journal={arXiv preprint arXiv:2503.21729},
year={2025}
}
6 commits
ReaRAG-20k is a reasoning-focused dataset designed for training the ReaRAG model. It contains approximately 20,000 multi-turn retrieval examples constructed from the QA datasets such as HotpotQA, MuSiQue, and Natural Questions (NQ).
Each instance follows a conversational format supporting reasoning and retrieval steps:
{
"messages": [{"role": "user", "content": "..."},
{"role": "assistant", "reasoning": "..."},
{"role": "observation", "content": "..."}, ...]
}
During sft, the loss is computed only on messages that contain the reasoning key, rather than the content key.
If you use this dataset in your research or projects, please consider citing our work:
@article{lee2025rearag,
title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
journal={arXiv preprint arXiv:2503.21729},
year={2025}
}
6 commits