m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning
  with Large Language Models
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.
67ecb60276900f68cd1df503
Xiaoke Huang
admin_assigned
2025-04-02T12:27:31.825Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63318b2349a9563915469f3b/zlbeB2997i8YkoyOTb9FL.jpeg
Xiaoke Huang
user
xk-huang
67ecb60276900f68cd1df504
Juncheng Wu
admin_assigned
2025-04-02T12:27:39.042Z
641a58d791e3376a0579f011
/avatars/bb4745bb83ac0ee094b43e1e5562d743.svg
Juncheng Wu
user
JunchengWu
67ecb60276900f68cd1df505
Hui Liu
67ecb60276900f68cd1df506
Xianfeng Tang
admin_assigned
2025-04-02T12:27:48.508Z
6465f6467ff8fcbef7d22513
/avatars/07992835c235fbb07016a0ea4f1d61cb.svg
Xianfeng Tang
user
xianft
67ecb60276900f68cd1df507
Yuyin Zhou
admin_assigned
2025-04-02T12:27:54.857Z
66c7fb4ce2c92fe5b132f314
/avatars/22d915fa339a70803c5c748255250256.svg
Yuyin Zhou
user
RitaCoding
m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning
  with Large Language Models
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.
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.
test-time scaling medical reasoning knowledge grounding medical knowledge reasoning token budget iterative prompts budget forcing medical QA medical benchmarks