Gen-RSI: Recursive Self-Improvement of Physical Video Generators through World Proxies

Despite producing visually realistic videos, video generation models often violate physical laws in the motion and interactions they depict. Existing post-training methods usually rely on offline physics datasets to encode physical priors which may not address the model’s current failures, or learned reward models that assess physical plausibility without providing explicit correction targets. We introduce **Gen-RSI**, a recursive self-improvement framework that constructs physical corrections from the model’s own generations through executable world proxies. Specifically, a coding agent reconstructs the generated scene and motion as a Blender program and uses physics simulation to diagnose and repair physical violations. Physically verified repairs are rendered to condition a frozen video teacher, whose predictions provide on-policy distillation targets for the student generator at its own denoising states. Across rounds, Gen-RSI rebuilds correction targets from the updated generator and refines its agentic diagnosis and repair procedures using feedback from prior attempts. After four-round training on Wan2.1-14B, Gen-RSI improves the PhyGenBench average score from 0.369 to 0.558 and the mean score across the mechanics, thermotics, and material dimensions of VBench-2.0 from 73.09% to 80.00%. View the supplementary material video at: https://anonymous-submission-21194.github.io/