用强化学习提升医疗多模态推理能力，让AI更懂复杂诊疗决策。
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
- 通过强化学习迭代优化，增强模型的医学推理能力
- 在图像诊断与视觉问答任务中显著提升准确率
- 适合医疗AI研究者与临床辅助系统开发者
通用医疗AI虽有进展，但复杂诊疗决策仍缺乏足够推理能力。本文提出GMAI-VL-R1，一种基于强化学习（RL）的多模态医疗推理模型，通过迭代训练优化决策过程，显著提升诊断准确率与临床支持能力。我们还设计了一种拒绝采样生成法，合成逐步推理数据，进一步提升模型泛化性。实验表明，经强化学习训练后，该模型在医学图像诊断与视觉问答任务中表现优异；仅靠监督微调时模型仅具基础记忆能力，而强化学习对真正泛化至关重要。本工作建立新评估基准，推动医疗推理模型发展。代码、数据与模型将开源于https://github.com/uni-medical/GMAI-VL-R1。
原文摘要 · Abstract (English)
Recent advances in general medical AI have made significant strides, but existing models often lack the reasoning capabilities needed for complex medical decision-making. This paper presents GMAI-VL-R1, a multimodal medical reasoning model enhanced by reinforcement learning (RL) to improve its reasoning abilities. Through iterative training, GMAI-VL-R1 optimizes decision-making, significantly boosting diagnostic accuracy and clinical support. We also develop a reasoning data synthesis method, generating step-by-step reasoning data via rejection sampling, which further enhances the model's generalization. Experimental results show that after RL training, GMAI-VL-R1 excels in tasks such as medical image diagnosis and visual question answering. While the model demonstrates basic memorization with supervised fine-tuning, RL is crucial for true generalization. Our work establishes new evaluation benchmarks and paves the way for future advancements in medical reasoning models. Code, data, and model will be released at \href{https://github.com/uni-medical/GMAI-VL-R1}{this link}.
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