Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning

Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically exhibit extremely sparse visual evidence to inform clinical decision-making.

We recognize that pruning visual tokens outside the grounding region greatly enhances medical reasoning.

However, a united RL framework for active visual token pruning (VTP) and medical multimodal reasoning remains unestablished.

Here, we propose a dual-stream RL framework, ViToS, to fulfill token pruning and question answering.

ViToS trains one policy model with two task branches, where one focuses on grounding while the other conducts token-sparse reasoning after VTP.

Furthermore, we solve the coupled policy learning problem by introducing the cross-feedback sequential optimization, avoiding gradient conflict and facilitating convergence of the shared policy model.

Evaluated on seven medical benchmarks, our method reduces visual tokens to 77% of the original sequence length while achieving a 108.27% relative performance on Lingshu-7B and 104.16% relative performance on HuatuoGPT-Vision-7B.

Overall, ViToS delivers superior performance and inference speedup, establishing an efficient paradigm for medical multimodal reasoning.