AI RESEARCH
Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought
arXiv CS.CV
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ArXi:2603.22847v1 Announce Type: new Multimodal Chain-of-Thought (CoT) reasoning requires large vision-language models to construct reasoning trajectories that interleave perceptual grounding with multi-step inference. However, existing Reinforcement Learning with Verifiable Rewards (RLVR) methods typically optimize reasoning at a coarse granularity, treating CoT uniformly without distinguishing their varying degrees of visual grounding.