RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning

• URL: http://arxiv.org/abs/2509.00974v2
• Date: Thu, 06 Nov 2025 01:32:56 GMT
• Title: RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning
• Authors: Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu, Chun-Chieh Liao, Fang-Ming Hung, Feng Liu,
• Abstract summary: Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference.<n>We propose Ranked Preference Reinforcement Optimization (RPRO), a novel framework that combines reinforcement learning with preference-driven reasoning refinement.
• Score: 5.493679122639688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reasoning chains that lack factual accuracy and clinical reliability. We propose Ranked Preference Reinforcement Optimization (RPRO), a novel framework that uniquely combines reinforcement learning with preference-driven reasoning refinement to enhance clinical chain-of-thought (CoT) performance. RPRO differentiates itself from prior approaches by employing task-adaptive reasoning templates and a probabilistic evaluation mechanism that aligns outputs with established clinical workflows, while automatically identifying and correcting low-quality reasoning chains. Unlike traditional pairwise preference methods, RPRO introduces a groupwise ranking optimization based on the Bradley-Terry model and incorporates KL-divergence regularization for stable training. Experiments on PubMedQA and MedQA-USMLE show consistent improvements over strong baselines. Remarkably, our 1.1B parameter model outperforms much larger 7B-13B models, including medical-specialized variants. These findings demonstrate that combining preference optimization with quality-driven refinement offers a scalable and effective approach to building more reliable, clinically grounded medical LLMs.

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