History Dropout for Long-Horizon Autoregressive Video Generation

Autoregressive video generators use previously generated content to maintain continuity, but this history can also carry errors into later predictions. We introduce History Dropout (), a history regularization method for long-horizon video generation. During training, varies historical value masks across denoising steps while retaining all keys for attention normalization. Inverse-retention scaling preserves the expected attention output for fixed, mask-independent inputs. At inference, each attention head independently samples historical key-value tokens, with sink and current-chunk tokens always retained. The two operations serve different purposes: training perturbs historical contributions without changing the softmax denominator, whereas inference uses shorter attention inputs. Applied to Self Forcing, Causal Forcing, and LongLive, improves visual quality and dynamics at both 60 and 240 seconds, while inference latency decreases from 0.72 to 0.64 seconds on a single NVIDIA H20 GPU. Please refer to https://anonymous.4open.science/w/hidrop-supplementary-2E19/ this link for our supplementary video.