通过强化学习增强放射学报告生成和视觉基础识别
Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning
AI总结：
本研究通过强化学习和思考机制提升放射学报告生成和视觉基础识别的性能，展示了在医学VLMs中RL作为SFT的有效补充。
摘要：
最近在视觉-语言模型（VLMs）方面的进展在多个方面提高了胸部X光片（CXR）的解读。然而，许多医学VLMs仅依赖于监督微调（SFT），这优化了下一个标记的预测，而没有评估回答质量。相比之下，强化学习（RL）可以结合任务特定的反馈，其与显式中间推理（
英文摘要：
Recent advances in vision-language models (VLMs) have improved Chest X-ray (CXR) interpretation in multiple aspects. However, many medical VLMs rely solely on supervised fine-tuning (SFT), which optimizes next-token prediction without evaluating answer quality. In contrast, reinforcement learning (RL) can incorporate task-specific feedback, and its combination with explicit intermediate reasoning ("thinking") has demonstrated substantial gains on verifiable math and coding tasks. To investigate the effects of RL and thinking in a CXR VLM, we perform large-scale SFT on CXR data to build an updated RadVLM based on Qwen3-VL, followed by a cold-start SFT stage that equips the model with basic thinking ability. We then apply Group Relative Policy Optimization (GRPO) with clinically grounded, task-specific rewards for report generation and visual grounding, and run matched RL experiments on both domain-specific and general-domain Qwen3-VL variants, with and without thinking. Across these settings, we find that while strong SFT remains crucial for high base performance, RL provides additional gains on both tasks, whereas explicit thinking does not appear to further improve results. Under a unified evaluation pipeline, the RL-optimized RadVLM models outperform their baseline counterparts and reach state-of-the-art performance on both report generation and grounding, highlighting clinically aligned RL as a powerful complement to SFT for medical VLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
