{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/gmai-vl-r1-harnessing-reinforcement-learning","title":"GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning","arxiv_id":"2504.01886","date":"2025-04-02","proceeding":null,"authors":["Yanzhou Su","Tianbin Li","Jiyao Liu","Chenglong Ma","Junzhi Ning","Cheng Tang","Sibo Ju","Jin Ye","Pengcheng Chen","Ming Hu","Shixiang Tang","Lihao Liu","Bin Fu","Wenqi Shao","Xiaowei Hu","Xiangwen Liao","Yuanfeng Ji","Junjun He"],"abstract":"Recent advances in general medical AI have made significant strides, but existing models often lack the reasoning capabilities needed for complex medical decision-making. This paper presents GMAI-VL-R1, a multimodal medical reasoning model enhanced by reinforcement learning (RL) to improve its reasoning abilities. Through iterative training, GMAI-VL-R1 optimizes decision-making, significantly boosting diagnostic accuracy and clinical support. We also develop a reasoning data synthesis method, generating step-by-step reasoning data via rejection sampling, which further enhances the model's generalization. Experimental results show that after RL training, GMAI-VL-R1 excels in tasks such as medical image diagnosis and visual question answering. While the model demonstrates basic memorization with supervised fine-tuning, RL is crucial for true generalization. Our work establishes new evaluation benchmarks and paves the way for future advancements in medical reasoning models. Code, data, and model will be released at \\href{https://github.com/uni-medical/GMAI-VL-R1}{this link}.","url_abs":"https://arxiv.org/abs/2504.01886v1","url_pdf":"https://arxiv.org/pdf/2504.01886v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gmai-vl-r1-harnessing-reinforcement-learning","repo_url":"https://github.com/uni-medical/gmai-vl-r1","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"memorization","task_name":"Memorization"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2504.01886","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}