Physics-Reinforced Distribution Matching Distillation for Autoregressive Video Generation

Autoregressive (AR) video diffusion distillation enables few-step streaming generation, but matching a teacher's distribution does not by itself ensure physically plausible dynamics over extended rollouts. We study how to evaluate and improve physical plausibility in distilled AR models. We first introduce PLV-Bench, an image-to-video benchmark comprising 125 real-world videos, across 16 physical phenomena and spanning at least 10 seconds, with a reference-guided, phase-level evaluation protocol. We then present PhysDMD which augments Distribution Matching Distillation with complementary supervision from a frozen latent world model. To avoid favoring trivially predictable motion, we calibrate the generated transition's prediction energy against its paired real trajectory. The resulting signal directly supervises the final deterministic denoising step and serves as a reward for group-relative optimization of intermediate stochastic transitions. On Physics-IQ-Verified, PhysDMD improves the Overall score from 25.31 to 28.10 over Vanilla-DMD while retaining four-step sampling and requiring no world-model evaluation at inference. Ablations examine the contributions of dynamics alignment, policy optimization, and training-data composition.