FineMedLM-o1 represents a significant advancement in medical AI by enhancing large language models' ability to handle complex clinical reasoning tasks through specialized training approaches.

• Combines high-quality synthetic medical data with long-form reasoning data for Supervised Fine-Tuning
• Employs Direct Preference Optimization to refine medical reasoning abilities
• Introduces Test-Time Training strategies for improved medical diagnosis and treatment planning
• Demonstrates superior performance on complex clinical scenarios including differential diagnosis

This research addresses critical gaps in existing medical LLMs, potentially transforming how AI supports healthcare decision-making by providing more reliable reasoning for complex patient cases.

FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training