构建37万条医学推理数据集，提升AI在临床问答中的逻辑能力
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
- 用多智能体生成+验证+优化流程，从175万条路径中提炼高质量数据
- 训练的ReasonMed-7B模型在PubMedQA上比前代小模型高4.17%，超LLaMA3.1-70B 4.60%
- 适合医疗AI研究者、医学大模型开发者使用，尤其关注推理链训练
基于大语言模型的推理能力在数学和编程领域表现优异，但在知识密集型医疗问答中的潜力尚未充分挖掘，且缺乏临床场景下的验证。为此，我们提出ReasonMed，目前规模最大的医学推理数据集，包含37万条高质量样本，由175万条初始推理路径经互补式大模型生成，并通过高效易-中-难（EMD）筛选流程精炼而成。该数据集通过多智能体生成、验证与优化流程构建，其中错误修正器会根据验证器识别出的问题步骤进行修正。基于ReasonMed，我们研究了有效的医学推理模型训练策略，发现将详细思维链（CoT）与简洁答案摘要结合，能获得最稳健的微调效果。在该数据集上训练的ReasonMed-7B模型创下新基准：其在PubMedQA上的表现超越先前所有子100亿参数模型4.17%，甚至超过LLaMA3.1-70B达4.60%。当扩展至ReasonMed-14B时，仍保持高度竞争力，表明其具备持续可扩展性。代码与数据集已开源。
原文摘要 · Abstract (English)
Reasoning-based large language models have excelled in mathematics and programming, yet their potential in knowledge-intensive medical question answering remains underexplored and insufficiently validated in clinical contexts. To bridge this gap, we introduce ReasonMed, the largest medical reasoning dataset to date, comprising 370k high-quality examples distilled from 1.75 million initial reasoning paths generated by complementary LLMs and curated through a cost-efficient easy-medium-difficult (EMD) pipeline. ReasonMed is built through a multi-agent generation, verification, and refinement process, in which an Error Refiner improves reasoning paths by correcting error-prone steps identified by a verifier. Using ReasonMed, we investigate effective strategies for training medical reasoning models and find that integrating detailed CoT reasoning with concise answer summaries yields the most robust fine-tuning results. Models trained on ReasonMed set a new benchmark: ReasonMed-7B surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%. When scaled to ReasonMed-14B, it remains highly competitive, underscoring consistent scaling potential. The codes and datasets are available at https://github.com/YuSun-Work/ReasonMed.
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