Baichuan-M2: Scaling Medical Capability with Large Verifier System
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.
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Baichuan-M2 Team
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Chengfeng Dou
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Chong Liu
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Fan Yang
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FanYang
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Fei Li
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Jiyuan Jia
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Mingyang Chen
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Mingyang Chen
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Qiang Ju
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Shuai Wang
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Shunya Dang
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Tianpeng Li
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李天鹏
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Xiangrong Zeng
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Yijie Zhou
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2025-09-03T08:26:54.451Z
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Jie
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Chenzheng Zhu
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Da Pan
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Fei Deng
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Guangwei Ai
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Guosheng Dong
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Hongda Zhang
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Jinyang Tai
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Jixiang Hong
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Kai Lu
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Linzhuang Sun
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Peidong Guo
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Peidong Guo
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Qian Ma
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Rihui Xin
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xinrihui
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Shihui Yang
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Shusen Zhang
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Yichuan Mo
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Zheng Liang
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Zhishou Zhang
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Hengfu Cui
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Zuyi Zhu
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Xiaochuan Wang
Baichuan-M2: Scaling Medical Capability with Large Verifier System
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.
A dynamic verification framework using reinforcement learning and a novel algorithm improves the performance of a large medical language model in real-world clinical decision-making.
large language models LLMs conversational capabilities reasoning capabilities healthcare medical LLMs USMLE real-world clinical decision-making dynamic verification framework interactive reinforcement learning Patient Simulator de-identified medical records Clinical Rubrics Generator multi-dimensional evaluation metrics Baichuan-M2 32B-parameter medical augmented reasoning model multi-stage reinforcement learning Group Relative Policy Optimization GRPO HealthBench HealthBench Hard benchmark GPT-5 Pareto front medical AI deployment