pith. sign in

arxiv: 2504.01886 · v1 · pith:7ABEW46Mnew · submitted 2025-04-02 · 💻 cs.CV

GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning

Yanzhou Su , Tianbin Li , Jiyao Liu , Chenglong Ma , Junzhi Ning , Cheng Tang , Sibo Ju , Jin Ye
show 10 more authors
Pengcheng Chen Ming Hu Shixiang Tang Lihao Liu Bin Fu Wenqi Shao Xiaowei Hu Xiangwen Liao Yuanfeng Ji Junjun He
This is my paper
classification 💻 cs.CV
keywords reasoningmedicalgmai-vl-r1modeldatadecision-makinggeneralizationlearning
0
0 comments X
read the original 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}.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Topo-R1: Detecting Topological Anomalies via Vision-Language Models

    cs.CV 2026-03 unverdicted novelty 7.0

    Topo-R1 fine-tunes a vision-language model using a topology-aware reward and GRPO to detect anomalies such as broken or spurious connections in tubular segmentation masks, outperforming standard VLMs.

  2. MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

    cs.CV 2026-04 unverdicted novelty 6.0

    MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.

  3. MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

    cs.CV 2026-04 unverdicted novelty 6.0

    MedSynapse-V proposes meta-query prior memorization, causal counterfactual refinement via RL, and dual-branch memory transition to evolve implicit diagnostic memories in medical VLMs and boost accuracy over chain-of-t...

  4. MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

    cs.CV 2026-04 unverdicted novelty 5.0

    MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought...

  5. MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

    cs.CV 2026-04 unverdicted novelty 4.0

    MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought ...