ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning

Yu Sun, Xingyu Qian, Xu Weiwen, ZHANG HAO, Chenghao Xiao, ll, Yu Rong, Wenbing Huang, Qifeng Bai, Tingyang Xu

ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning: 103 upvotes on Hugging Face Daily Papers, #1 of 41 papers on 2025-06-13. Day-by-day upvote history.

Though reasoning-based large language models (LLMs) have excelled in mathematics and programming, their capabilities in knowledge-intensive medical question answering remain underexplored. To address this, we introduce ReasonMed, the largest medical reasoning dataset, comprising 370k high-quality examples distilled from 1.7 million initial reasoning paths generated by various LLMs. ReasonMed is constructed through a multi-agent verification and refinement process, where we design an Error Refiner to enhance the reasoning paths by identifying and correcting error-prone steps flagged by a verifier. Leveraging ReasonMed, we systematically investigate best practices for training medical reasoning models and find that combining detailed Chain-of-Thought (CoT) reasoning with concise answer summaries yields the most effective fine-tuning strategy. Based on this strategy, we train ReasonMed-7B, which sets a new benchmark for sub-10B models, outperforming the prior best by 4.17\% and even exceeding LLaMA3.1-70B on PubMedQA by 4.60\%.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.