Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports
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.
| Authors | Yishu Wei, Yi Lin, Adam Flanders et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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