ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical
  Reasoning
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\%.
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Yu Sun
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Yu Sun
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Xingyu Qian
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Weiwen Xu
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Xu Weiwen
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Hao Zhang
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ZHANG HAO
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Chenghao Xiao
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chenghao xiao
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Long Li
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Yu Rong
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Yu Rong
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Swrooy
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Wenbing Huang
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Qifeng Bai
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Qifeng Bai
user
Qifeng-Bai
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Tingyang Xu
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical
  Reasoning
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\%.
ReasonMed, a large medical reasoning dataset, enhances the accuracy of medical question answering models by combining detailed reasoning paths with concise summaries, setting new benchmarks for model performance.
reasoning-based large language models LLMs medical question answering ReasonMed multi-agent verification Error Refiner Chain-of-Thought CoT reasoning ReasonMed-7B PubMedQA