Title: MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentation Show Chinese title

Title: MedFact-R1：通过伪标签增强实现事实性医学推理

Abstract: Ensuring factual consistency and reliable reasoning remains a critical challenge for medical vision-language models. We introduce MEDFACT-R1, a two-stage framework that integrates external knowledge grounding with reinforcement learning to improve the factual medical reasoning. The first stage uses pseudo-label supervised fine-tuning (SFT) to incorporate external factual expertise; while the second stage applies Group Relative Policy Optimization (GRPO) with four tailored factual reward signals to encourage self-consistent reasoning. Across three public medical QA benchmarks, MEDFACT-R1 delivers up to 22.5% absolute improvement in factual accuracy over previous state-of-the-art methods. Ablation studies highlight the necessity of pseudo-label SFT cold start and validate the contribution of each GRPO reward, underscoring the synergy between knowledge grounding and RL-driven reasoning for trustworthy medical AI. Codes are released at https://github.com/Garfieldgengliang/MEDFACT-R1. Show Chinese abstract

Abstract: 确保事实一致性与可靠推理仍然是医学视觉-语言模型面临的关键挑战。 我们引入了MEDFACT-R1，这是一种两阶段框架，将外部知识定位与强化学习相结合，以提高事实性医学推理。 第一阶段使用伪标签监督微调（SFT）来整合外部事实专业知识；而第二阶段应用带有四个定制事实奖励信号的组相对策略优化（GRPO），以鼓励自我一致的推理。 在三个公开的医学问答基准上，MEDFACT-R1在事实准确性方面相比之前最先进的方法提高了高达22.5%。 消融研究强调了伪标签SFT冷启动的必要性，并验证了每个GRPO奖励的贡献，突显了知识定位与RL驱动推理之间的协同作用，以实现可信赖的医学AI。 代码已发布在https://github.com/Garfieldgengliang/MEDFACT-R1。