AI RESEARCH
Wan-R1: Verifiable-Reinforcement Learning for Video Reasoning
arXiv CS.CV
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ArXi:2603.27866v1 Announce Type: new Video generation models produce visually coherent content but struggle with tasks requiring spatial reasoning and multi-step planning. Reinforcement learning (RL) offers a path to improve generalization, but its effectiveness in video reasoning hinges on reward design -- a challenge that has received little systematic study. We investigate this problem by adapting Group Relative Policy Optimization (GRPO) to flow-based video models and