CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making

• URL: http://arxiv.org/abs/2506.12849v1
• Date: Sun, 15 Jun 2025 13:42:46 GMT
• Title: CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
• Authors: Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Sibo Song, Yang Feng, Jimeng Sun, Jian Wu, Zuozhu Liu,
• Abstract summary: We introduce Med-Zero-17K, a curated dataset for pure RL-based training, encompassing over 30 medical image modalities and 24 clinical tasks.<n>We propose a novel large-scale RL framework for Med-VLMs, which integrates rewards to ensure fidelity between perception and reasoning, consistency in reasoning-to-answer, and rule-based accuracy for final responses.
• Score: 42.28216499263317
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging data, logical reasoning grounded in visual input and textual questions, and coherent answer derivation from the reasoning process. Recent advances in general vision-language models (VLMs) show that large-scale reinforcement learning (RL) could significantly enhance both reasoning capabilities and overall model performance. However, their application in medical domains is hindered by two fundamental challenges: 1) misalignment between perceptual understanding and reasoning stages, and 2) inconsistency between reasoning pathways and answer generation, both compounded by the scarcity of high-quality medical datasets for effective large-scale RL. In this paper, we first introduce Med-Zero-17K, a curated dataset for pure RL-based training, encompassing over 30 medical image modalities and 24 clinical tasks. Moreover, we propose a novel large-scale RL framework for Med-VLMs, Consistency-Aware Preference Optimization (CAPO), which integrates rewards to ensure fidelity between perception and reasoning, consistency in reasoning-to-answer derivation, and rule-based accuracy for final responses. Extensive experiments on both in-domain and out-of-domain scenarios demonstrate the superiority of our method over strong VLM baselines, showcasing strong generalization capability to 3D Med-VQA benchmarks and R1-like training paradigms.

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