Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports

Yishu Wei; Yi Lin; Adam Flanders; George Shih; Yifan Peng

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arXiv · 2604.19060

Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports

Yishu Wei·Yi Lin·Adam Flanders·George Shih·Yifan Peng

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Abstract

Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format without reasoning supervision. Across three radiologist-annotated datasets, SFT outperformed baselines and GRPO further improved classification and enhanced reasoning recall and comprehensiveness.

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Yishu Wei, Yi Lin, Adam Flanders, George Shih, Yifan Peng. 2026-04-21. Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports. https://arxiv.org/abs/2604.19060

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