UCSC-VLAA/m1-7B-1K

m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning in Large Language Models

A simple test-time scaling strategy, with minimal fine-tuning, can unlock strong medical reasoning within large language models.

⚡ Introduction

Hi! Welcome to the huggingface repository for m1 (https://github.com/UCSC-VLAA/m1)!

m1 is a medical LLM designed to enhance reasoning through efficient test-time scaling. It enables lightweight models to match or exceed the performance of much larger counterparts by extending inference-time “thinking.” Unlike methods that rely on complex RL or expert supervision, m1 achieves strong results through:

• Fine-tuning on a small, high-quality set of verified medical reasoning examples, showing that even with just 1K–23K examples, m1-7B surpasses models like HuatuoGPT-o1-7B and UltraMedical-8B, and m1-32B rivals 70B-scale models.

• Scaling reasoning at inference using token budgets, which consistently improves performance across medical QA tasks—up to an optimal ~4K token budget, beyond which performance may degrade due to overthinking.

• Identifying medical knowledge as the key bottleneck, revealing that additional reasoning alone cannot overcome knowledge gaps; instead, improvements require better data quality and increased model capacity.