MedGR$^2$: Breaking the Data Barrier for Medical Reasoning via Generative Reward Learning

• URL: http://arxiv.org/abs/2508.20549v1
• Date: Thu, 28 Aug 2025 08:41:32 GMT
• Title: MedGR$^2$: Breaking the Data Barrier for Medical Reasoning via Generative Reward Learning
• Authors: Weihai Zhi, Jiayan Guo, Shangyang Li,
• Abstract summary: Supervised Fine-Tuning (SFT) on existing datasets often leads to poor generalization on unseen modalities and tasks.<n>We introduce Generative Reward Learning for Medical Reasoning (MedGR$2$), a novel framework that creates a self-improving virtuous cycle.<n>Our experiments demonstrate that SFT with MedGR$2$-produced data already surpasses baselines trained on large-scale, human-curated datasets.
• Score: 4.579424650757833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of Vision-Language Models (VLMs) in medicine is critically hampered by the scarcity of high-quality, expert-annotated data. Supervised Fine-Tuning (SFT) on existing datasets often leads to poor generalization on unseen modalities and tasks, while Reinforcement Learning (RL), a promising alternative, is stymied by the lack of reliable reward signals in this data-scarce domain. To break this impasse, we introduce Generative Reward Learning for Medical Reasoning (MedGR$^2$), a novel framework that creates a self-improving virtuous cycle. MedGR$^2$ co-develops a data generator and a reward model, enabling the automated, continuous creation of high-quality, multi-modal medical data that serves as both a superior training source for SFT and RL. Our experiments demonstrate that SFT with MedGR$^2$-produced data already surpasses baselines trained on large-scale, human-curated datasets. Crucially, when leveraging this data for RL via Group Relative Policy Optimization (GRPO), our model achieves state-of-the-art cross-modality and cross-task generalization, significantly outperforming specialized RL-based methods. Furthermore, our compact model, empowered by MedGR$^2$, achieves performance competitive with foundation models possessing over 10 times more parameters. MedGR$^2$ presents a new paradigm for data-efficient learning in high-stakes domains, transforming the problem from data scarcity to data generation and unlocking the full potential of RL for building truly generalizable medical AI.

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