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HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

Summary

This research introduces HuatuoGPT-o1, a large language model (LLM) specialized for complex medical reasoning. The model is trained using a novel two-stage approach: first, a search-based strategy learns complex reasoning trajectories from a newly created dataset of 40,000 verifiable medical problems; second, reinforcement learning further refines this ability using verifier feedback. HuatuoGPT-o1 significantly outperforms existing general and medical LLMs on various benchmarks, demonstrating the effectiveness of the proposed method. The study also explores the reliability of the LLM-based verifier and investigates the impact of different reasoning strategies and RL algorithms. Finally, the approach is successfully extended to the Chinese medical domain, highlighting its broad applicability.

本研究提出了HuatuoGPT-o1，一种专门用于复杂医学推理的大型语言模型（LLM）。该模型采用了一种新颖的两阶段训练方法：首先，通过基于搜索的策略，从新创建的包含40,000个可验证医学问题的数据集中学习复杂的推理轨迹；其次，通过强化学习（RL）使用验证器反馈进一步优化该能力。HuatuoGPT-o1 在多个基准测试中显著优于现有的通用和医学 LLM，验证了所提方法的有效性。研究还探讨了基于 LLM 的验证器的可靠性，并研究了不同推理策略和强化学习算法的影响。最后，该方法成功扩展到中文医学领域，突显了其广泛的应用潜力。

原文链接：https://arxiv.org/abs/2412.18925