Rethinking Self-forcing DMD from a Score Sharpness Perspective

Self-Forcing with Distribution Matching Distillation (DMD) enables real-time autoregressive video generation, but its outputs can exhibit insufficient motion and degraded visual quality. We revisit these limitations from a score sharpness perspective and use this perspective to motivate changes to the DMD training signal. An illustrative one-dimensional Gaussian mixture connects the noise level to local variations of the score, suggesting a timestep curriculum that gradually shifts training from high-noise to low-noise regimes. We further introduce a timestep offset with antithetic sampling, motivated by the hypothesis that small changes to the teacher’s input timestep may mitigate some prediction errors, and adopt the projection mechanism of Adaptive Projected Guidance (APG) to downweight the parallel guidance component. These three interventions act during training and leave the student architecture and inference procedure unchanged. Experiments on Wan2.1-T2V-1.3B show higher Total, Quality, and Semantic scores than Self-Forcing under the reported evaluation protocol, while preserving real-time throughput. Our method obtains the highest reported Total and Quality scores among the compared methods, with a lower Semantic score than Causal Forcing. Component ablations support the benefits of combining the interventions, including an increase in Dynamic Degree from 23.12 to 56.48 on the ablation prompt suite.