Arxiv paper - Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

In this episode, we discuss Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models by Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, Shaohui Lin. The paper aims to enhance the reasoning abilities of Multimodal Large Language Models (MLLMs) using reinforcement learning (RL). To overcome the lack of high-quality multimodal reasoning data, the authors develop Vision-R1 by creating a 200K multimodal Chain-of-Thought dataset without human annotations. They further improve Vision-R1’s reasoning through Progressive Thinking Suppression Training and Group Relative Policy Optimization on a specialized 10K multimodal math dataset.