MedS$^3$: Towards Medical Small Language Models with Self-Evolved Slow Thinking

Medical language models (MLMs) have become pivotal in advancing medical natural language processing. However, prior models that rely on pre-training or supervised fine-tuning often exhibit low data efficiency and limited practicality in real-world clinical applications. While OpenAI's o1 highlights test-time scaling in mathematics, attempts to replicate this approach in medicine typically distill responses from GPT-series models to open-source models, focusing primarily on multiple-choice tasks. This strategy, though straightforward, neglects critical concerns like data privacy and realistic deployment in clinical settings. In this work, we present a deployable, small-scale medical reasoning system, MedS3, designed for long-chain reasoning in clinical tasks using a self-evolution paradigm. Starting with a seed dataset of around 8,000 instances spanning five domains and 16 datasets, we prompt a base policy model to perform Monte Carlo Tree Search (MCTS) to construct rule-verifiable reasoning chains. Each reasoning step is assigned an evolution rollout value, allowing verified trajectories to train the policy model and the process reward model (PRM). During inference, the policy model generates multiple responses, and the reward model selects the one with a newly proposed PRM-guided Vote-Sum (P-VS) strategy. Experiments on eleven evaluation datasets demonstrate that MedS3 outperforms not only the prior strongest medical model by 6.59, but also 32B-level general reasoning models by 8.71 points. Code and data are available at https://github.com/pixas/MedSSS.