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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02208 |
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| _version_ | 1866918134272753664 |
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| author | M2 Team Dou, Chengfeng Liu, Chong Yang, Fan Li, Fei Jia, Jiyuan Chen, Mingyang Ju, Qiang Wang, Shuai Dang, Shunya Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhu, Chenzheng Pan, Da Deng, Fei Ai, Guangwei Dong, Guosheng Zhang, Hongda Tai, Jinyang Hong, Jixiang Lu, Kai Sun, Linzhuang Guo, Peidong Ma, Qian Xin, Rihui Yang, Shihui Zhang, Shusen Mo, Yichuan Liang, Zheng Zhang, Zhishou Cui, Hengfu Zhu, Zuyi Wang, Xiaochuan |
| author_facet | M2 Team Dou, Chengfeng Liu, Chong Yang, Fan Li, Fei Jia, Jiyuan Chen, Mingyang Ju, Qiang Wang, Shuai Dang, Shunya Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhu, Chenzheng Pan, Da Deng, Fei Ai, Guangwei Dong, Guosheng Zhang, Hongda Tai, Jinyang Hong, Jixiang Lu, Kai Sun, Linzhuang Guo, Peidong Ma, Qian Xin, Rihui Yang, Shihui Zhang, Shusen Mo, Yichuan Liang, Zheng Zhang, Zhishou Cui, Hengfu Zhu, Zuyi Wang, Xiaochuan |
| contents | As large language models (LLMs) advance in conversational and reasoning capabilities, their practical application in healthcare has become a critical research focus. However, there is a notable gap between the performance of medical LLMs on static benchmarks such as USMLE and their utility in real-world clinical decision-making. This discrepancy arises because traditional exams fail to capture the dynamic, interactive nature of medical consultations. To address this challenge, we introduce a novel dynamic verification framework that moves beyond static answer verifier, establishing a large-scale, high-fidelity interactive reinforcement learning system. Our framework comprises two key components: a Patient Simulator that creates realistic clinical environments using de-identified medical records, and a Clinical Rubrics Generator that dynamically produces multi-dimensional evaluation metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter medical augmented reasoning model trained through a multi-stage reinforcement learning strategy with an improved Group Relative Policy Optimization (GRPO) algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other open-source models and most advanced closed-source counterparts, achieving a score above 32 on the challenging HealthBench Hard benchmark-previously exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier system is essential for aligning LLM capabilities with practical clinical applications, establishing a new Pareto front in the performance-parameter trade-off for medical AI deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02208 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Baichuan-M2: Scaling Medical Capability with Large Verifier System M2 Team Dou, Chengfeng Liu, Chong Yang, Fan Li, Fei Jia, Jiyuan Chen, Mingyang Ju, Qiang Wang, Shuai Dang, Shunya Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhu, Chenzheng Pan, Da Deng, Fei Ai, Guangwei Dong, Guosheng Zhang, Hongda Tai, Jinyang Hong, Jixiang Lu, Kai Sun, Linzhuang Guo, Peidong Ma, Qian Xin, Rihui Yang, Shihui Zhang, Shusen Mo, Yichuan Liang, Zheng Zhang, Zhishou Cui, Hengfu Zhu, Zuyi Wang, Xiaochuan Machine Learning Artificial Intelligence As large language models (LLMs) advance in conversational and reasoning capabilities, their practical application in healthcare has become a critical research focus. However, there is a notable gap between the performance of medical LLMs on static benchmarks such as USMLE and their utility in real-world clinical decision-making. This discrepancy arises because traditional exams fail to capture the dynamic, interactive nature of medical consultations. To address this challenge, we introduce a novel dynamic verification framework that moves beyond static answer verifier, establishing a large-scale, high-fidelity interactive reinforcement learning system. Our framework comprises two key components: a Patient Simulator that creates realistic clinical environments using de-identified medical records, and a Clinical Rubrics Generator that dynamically produces multi-dimensional evaluation metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter medical augmented reasoning model trained through a multi-stage reinforcement learning strategy with an improved Group Relative Policy Optimization (GRPO) algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other open-source models and most advanced closed-source counterparts, achieving a score above 32 on the challenging HealthBench Hard benchmark-previously exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier system is essential for aligning LLM capabilities with practical clinical applications, establishing a new Pareto front in the performance-parameter trade-off for medical AI deployment. |
| title | Baichuan-M2: Scaling Medical Capability with Large Verifier System |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.02208 |