![[2504.00869] m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2504.00869] m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models
Summary
This article explores the effectiveness of test-time scaling for enhancing medical reasoning in large language models, presenting the m1 approach that achieves state-of-the-art performance with smaller models.
Why It Matters
As large language models (LLMs) become integral in medical applications, understanding their reasoning capabilities is crucial. This research highlights how test-time scaling can improve medical reasoning, potentially influencing future AI developments in healthcare.
Key Takeaways
- Test-time scaling enhances medical reasoning capabilities of LLMs.
- The m1 approach achieves state-of-the-art performance with models under 10B parameters.
- An optimal reasoning token budget of 4K is identified for effective performance.
- Increasing data scale and quality is essential for improving medical knowledge grounding.
- Overthinking can degrade performance, indicating a need for balanced reasoning depth.
Computer Science > Computation and Language arXiv:2504.00869 (cs) [Submitted on 1 Apr 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models Authors:Xiaoke Huang, Juncheng Wu, Hui Liu, Xianfeng Tang, Yuyin Zhou View a PDF of the paper titled m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models, by Xiaoke Huang and 4 other authors View PDF HTML (experimental) Abstract:Test-time scaling has emerged as a powerful technique for enhancing the reasoning capabilities of large language models. However, its effectiveness in medical reasoning remains uncertain, as the medical domain fundamentally differs from mathematical tasks in terms of knowledge representation and decision-making processes. In this paper, we provide the first comprehensive investigation of test-time scaling for medical reasoning and present m1, a simple yet effective approach that increases a model's medical reasoning capability at inference. Our evaluation across diverse medical tasks demonstrates that test-time scaling consistently enhances medical reasoning, enabling lightweight fine-tuned models under 10B parameters to establish new state-of-the-art performance, while our 32B model rivals previous 70B-scale medical LLMs. However, we identify an optimal reasoning token budget of approximately 4K, beyond which performance may degrade due to overthinking. Budget forci...
