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

arXiv: https://arxiv.org/abs/2504.00869 · HTML: https://mlsanity.com/paper/2504.00869/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://mlsanity.com/paper/2203.14371/) (2022)
- [Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model](https://mlsanity.com/paper/2503.24290/) (2025)
- [L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning](https://mlsanity.com/paper/2503.04697/) (2025)
- [HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs](https://mlsanity.com/paper/2412.18925/) (2024)
- [MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding](https://mlsanity.com/paper/2501.18362/) (2025)
- [Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning](https://mlsanity.com/paper/2502.18080/) (2025)
- [Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions](https://mlsanity.com/paper/2402.18060/) (2024)
- [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://mlsanity.com/paper/1906.04701/) (2019)
- [Towards Building Multilingual Language Model for Medicine](https://mlsanity.com/paper/2402.13963/) (2024)
- [Med42-v2: A Suite of Clinical LLMs](https://mlsanity.com/paper/2408.06142/) (2024)
- [UltraMedical: Building Specialized Generalists in Biomedicine](https://mlsanity.com/paper/2406.03949/) (2024)
- [Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions](https://mlsanity.com/paper/2408.00727/) (2024)
- [A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?](https://mlsanity.com/paper/2409.15277/) (2024)
- [DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning](https://mlsanity.com/paper/2309.00859/) (2023)

## Built on by (most cited first, 30 of 46)

- [Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL](https://mlsanity.com/paper/2505.17952/) (2025)
- [MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning](https://mlsanity.com/paper/2505.16964/) (2025)
- [X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains](https://mlsanity.com/paper/2505.03981/) (2025)
- [MedVLThinker: Simple Baselines for Multimodal Medical Reasoning](https://mlsanity.com/paper/2508.02669/) (2025)
- [MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science](https://mlsanity.com/paper/2506.04405/) (2025)
- [Disentangling Reasoning and Knowledge in Medical Large Language Models](https://mlsanity.com/paper/2505.11462/) (2025)
- [Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum](https://mlsanity.com/paper/2510.00526/) (2025)
- [Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains](https://mlsanity.com/paper/2506.02126/) (2025)
- [InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training](https://mlsanity.com/paper/2510.15859/) (2025)
- [Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications](https://mlsanity.com/paper/2508.00669/) (2025)
- [Recursive Multi-Agent Systems](https://mlsanity.com/paper/2604.25917/) (2026)
- [Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning](https://mlsanity.com/paper/2506.04611/) (2025)
- [Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning](https://mlsanity.com/paper/2506.12307/) (2025)
- [Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning](https://mlsanity.com/paper/2603.03437/) (2026)
- [How Inference Compute Shapes Frontier LLM Evaluation](https://mlsanity.com/paper/2606.17930/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning](https://mlsanity.com/paper/2603.27820/) (2026)
- [SSR: Speculative Parallel Scaling Reasoning in Test-time](https://mlsanity.com/paper/2505.15340/) (2025)
- [Learning a Continue-Thinking Token for Enhanced Test-Time Scaling](https://mlsanity.com/paper/2506.11274/) (2025)
- [Understanding the Role of Training Data in Test-Time Scaling](https://mlsanity.com/paper/2510.03605/) (2025)
- [Medical Reasoning with Large Language Models: A Survey and MR-Bench](https://mlsanity.com/paper/2604.08559/) (2026)
- [ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning](https://mlsanity.com/paper/2605.20176/) (2026)
- [Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLMs and VLMs](https://mlsanity.com/paper/2506.13102/) (2025)
- [Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data](https://mlsanity.com/paper/2508.01450/) (2025)
- [Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives](https://mlsanity.com/paper/2602.11424/) (2026)
- [Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models](https://mlsanity.com/paper/2603.01293/) (2026)
- [MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs](https://mlsanity.com/paper/2605.07305/) (2026)
- [On the Robustness of Answer Formats in Medical Reasoning Models](https://mlsanity.com/paper/2509.20866/) (2025)
- [AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification](https://mlsanity.com/paper/2509.24560/) (2025)
- [Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs](https://mlsanity.com/paper/2510.25867/) (2025)

## Abstract (arXiv)

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.