Wan-R1: Verifiable-Reinforcement Learning for Generalizable Video Reasoning

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.WeinvestigatethisproblembyadaptingGroupRelativePolicyOptimization (GRPO)toflow-basedvideomodelsandtrainingthemonmaze-solvingandrobotic navigationtasks.Wefirstshowthatmultimodalrewardmodelsfailcatastrophically in this setting. To address this, we design verifiable reward functions grounded in objective task metrics. For structured game environments, we introduce a multicomponent trajectory reward that is verifiable against ground-truth optimal paths. For robotic navigation, where ground truth is given only as a reference rollout, we propose an embedding-level reference-anchored reward. Our experiments show that RL fine-tuning with verifiable rewards improves generalization. For example, on complex 3D mazes, our model improves exact match accuracy by 29.1% over the SFT baseline, and on trap-avoidance tasks by 51.4%. Our systematic reward analysis reveals that verifiable rewards are critical for stable training, while multimodal reward models could lead to degenerate solutions. These findings establish verifiable reward design as a key enabler for robust video reasoning.