PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference

Autoregressive video diffusion models have demonstrated re-markable progress, yet they remain bottlenecked by intractable linearKV-cache growth, temporal repetition, and compounding errors duringlong-video generation. To address these challenges, we present Pack-Forcing, a unified framework that efficiently manages the generationhistory through a novel three-partition KV-cache strategy. Specifically,we categorize the historical context into three distinct types: (1) Sinktokens, which preserve early anchor frames at full resolution to maintainglobal semantics; (2) Mid tokens, which achieve a massive spatiotempo-ral compression (∼32× token reduction) via a dual-branch network fusingprogressive 3D convolutions with low-resolution VAE re-encoding; and(3) Recent tokens, kept at full resolution to ensure local temporal co-herence. To strictly bound the memory footprint without sacrificing qual-ity, we introduce a dynamic top-k context selection mechanism for themid tokens, coupled with a continuous Temporal RoPE Adjustment thatseamlessly re-aligns position gaps caused by dropped tokens with negli-gible overhead. Empowered by this principled hierarchical context com-pression, PackForcing can generate coherent 2-minute, 832×480 videosat 14–16 FPS on a single H200 GPU. It achieves a bounded KV cacheenables a remarkable 24× temporal extrapolation (5 s → 120 s), oper-ating effectively trained on merely 5-second clips. Extensive results onVBench demonstrate state-of-the-art temporal consistency (26.07) anddynamic degree (56.25), proving that short-video supervision is sufficientfor high-quality, long-video synthesis.