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

• URL: http://arxiv.org/abs/2504.00869v1
• Date: Tue, 01 Apr 2025 14:57:43 GMT
• 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,
• Abstract summary: We provide the first comprehensive investigation of test-time scaling for medical reasoning and present m1.<n>Our evaluation across diverse medical tasks demonstrates that test-time scaling consistently enhances medical reasoning.<n>We find that increasing data scale, improving data quality, and expanding model capacity consistently enhance medical knowledge grounding.
• Score: 21.849783391186754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning [27.827761004918106]
  This work explores the potential of inference-time scaling in large language models (LLMs) for medical reasoning tasks.<n>With a modest training set of 500 samples, our model yields substantial performance improvements of 6%-11%.
  arXiv  Detail & Related papers  (2025-01-11T07:10:23Z)
• LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models [18.6994780408699]
  Large Language Models (LLMs) face significant challenges in medical question answering.<n>We propose a novel approach incorporating similar case generation within a multi-agent medical question-answering system.<n>Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data.
  arXiv  Detail & Related papers  (2024-12-31T19:55:45Z)
• Superhuman performance of a large language model on the reasoning tasks of a physician [10.043418251604624]
  Performance of large language models (LLMs) on medical tasks has traditionally been evaluated using multiple choice question benchmarks.<n>We evaluate OpenAI's o1-preview model, a model developed to increase run-time via chain of thought processes prior to generating a response.
  arXiv  Detail & Related papers  (2024-12-14T14:46:18Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation. Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Explainable Diagnosis Prediction through Neuro-Symbolic Integration [11.842565087408449]
  We use neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Our models, particularly $M_textmulti-pathway$ and $M_textcomprehensive$, demonstrate superior performance over traditional models. These findings highlight the potential of neuro-symbolic approaches in bridging the gap between accuracy and explainability in healthcare AI applications.
  arXiv  Detail & Related papers  (2024-10-01T22:47:24Z)
• Unified Uncertainty Estimation for Cognitive Diagnosis Models [70.46998436898205]
  We propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. We decompose the uncertainty of diagnostic parameters into data aspect and model aspect. Our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
  arXiv  Detail & Related papers  (2024-03-09T13:48:20Z)
• Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models [73.79091519226026]
  Uncertainty of Thoughts (UoT) is an algorithm to augment large language models with the ability to actively seek information by asking effective questions. In experiments on medical diagnosis, troubleshooting, and the 20 Questions game, UoT achieves an average performance improvement of 38.1% in the rate of successful task completion.
  arXiv  Detail & Related papers  (2024-02-05T18:28:44Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.