title: m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning
    with Large Language Models
summary: 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 forcing, which extends test-time computation through iterative prompts,
  helps models double-check answers but does not necessarily improve the overall
  medical QA performance and, in some cases, even introduces errors into
  previously correct responses. Our case-by-case analysis identifies insufficient
  medical knowledge as a key bottleneck that prevents further performance gains
  through test-time scaling. We find that increasing data scale, improving data
  quality, and expanding model capacity consistently enhance medical knowledge
  grounding, enabling continued performance improvements, particularly on
  challenging medical benchmarks where smaller models reach saturation. These
  findings underscore fundamental differences between medical and mathematical
  reasoning in LLMs, highlighting that enriched medical knowledge, other than
  increased reasoning depth alone, is essential for realizing the benefits of
  test-time scaling.
paper_authors._id: 67ecb60276900f68cd1df503
paper_authors.name: Xiaoke Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:27:31.825Z
paper_authors._id: 63318b2349a9563915469f3b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63318b2349a9563915469f3b/zlbeB2997i8YkoyOTb9FL.jpeg
paper_authors.fullname: Xiaoke Huang
paper_authors.type: user
paper_authors.user: xk-huang
paper_authors._id: 67ecb60276900f68cd1df504
paper_authors.name: Juncheng Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:27:39.042Z
paper_authors._id: 641a58d791e3376a0579f011
paper_authors.avatarUrl: /avatars/bb4745bb83ac0ee094b43e1e5562d743.svg
paper_authors.fullname: Juncheng Wu
paper_authors.type: user
paper_authors.user: JunchengWu
paper_authors._id: 67ecb60276900f68cd1df505
paper_authors.name: Hui Liu
paper_authors._id: 67ecb60276900f68cd1df506
paper_authors.name: Xianfeng Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:27:48.508Z
paper_authors._id: 6465f6467ff8fcbef7d22513
paper_authors.avatarUrl: /avatars/07992835c235fbb07016a0ea4f1d61cb.svg
paper_authors.fullname: Xianfeng Tang
paper_authors.type: user
paper_authors.user: xianft
paper_authors._id: 67ecb60276900f68cd1df507
paper_authors.name: Yuyin Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:27:54.857Z
paper_authors._id: 66c7fb4ce2c92fe5b132f314
paper_authors.avatarUrl: /avatars/22d915fa339a70803c5c748255250256.svg
paper_authors.fullname: Yuyin Zhou
paper_authors.type: user
paper_authors.user: RitaCoding
paper_title: m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning
    with Large Language Models
paper_summary: 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 forcing, which extends test-time computation through iterative prompts,
  helps models double-check answers but does not necessarily improve the overall
  medical QA performance and, in some cases, even introduces errors into
  previously correct responses. Our case-by-case analysis identifies insufficient
  medical knowledge as a key bottleneck that prevents further performance gains
  through test-time scaling. We find that increasing data scale, improving data
  quality, and expanding model capacity consistently enhance medical knowledge
  grounding, enabling continued performance improvements, particularly on
  challenging medical benchmarks where smaller models reach saturation. These
  findings underscore fundamental differences between medical and mathematical
  reasoning in LLMs, highlighting that enriched medical knowledge, other than
  increased reasoning depth alone, is essential for realizing the benefits of
  test-time scaling.
paper_ai_summary: Test-time scaling enhances medical reasoning in large language models, enabling smaller models to achieve state-of-the-art performance with an optimal reasoning token budget, while emphasizing the importance of medical knowledge grounding.
paper_ai_keywords: test-time scaling medical reasoning knowledge grounding medical knowledge reasoning token budget iterative prompts budget forcing medical QA medical benchmarks