AGENTIC-SYSTEMSApril 22, 20261 min read6 views

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

Researchers proposed a two-stage approach combining SFT and GRPO to enhance LLM accuracy and reasoning in radiology. The method successfully improves disease classification and reasoning comprehensiveness without requiring explicit reasoning supervision.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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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Source: arXiv cs.AI Recent

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