Video Models Can Reason with Verifiable Rewards

Researchers introduced VideoRLVR, a reinforcement-learning framework for video diffusion models focused on verifiable reasoning tasks.

It uses rule-based rewards, dense decomposed feedback, and early-step optimization to improve performance on Maze, FlowFree, and Sokoban.

The method outperformed supervised baselines and other evaluated models on procedurally generated benchmarks.

The work suggests video generators can be trained to obey explicit spatial, temporal, and logical constraints, not just produce realistic visuals.