自梯度强制：原生长视频外推

摘要

近期，自回归视频扩散方法逐渐以“自我强制”（Self Forcing）为基础构建，即学生模型基于自身展开（rollout）生成的历史序列，而非真实视频上下文进行训练。这减少了曝光偏差，但历史的键值缓存仍仅作为冻结的展开状态被后续帧使用。因此，后续帧的损失无法监督早期生成的潜变量应如何被写入更有用的键与值，以服务于后续视频潜变量的生成。我们将此问题称为“历史上下文-梯度鸿沟”。为此，我们提出“自梯度强制”（Self Gradient Forcing, SGF），一种双遍训练策略，无需通过完整串行展开进行反向传播，即可恢复缺失的监督信号。第一遍执行无梯度的自回归展开以匹配推理过程，并在采样的去噪退出步中，记录自生成上下文以及输入模型的噪声潜变量。第二遍针对记录的退出步进行并行的上下文梯度重建。生成的上下文作为梯度停止的干净潜变量输入，而模型重新计算上下文的键值表示以及未来到上下文的因果注意力。由此，SGF在原生的自回归训练目标中提供了缺失的记忆写入监督，利用未来视频潜变量的损失来训练模型将上下文编码为更有效的因果记忆。在多种初始化条件下，针对长程帧级和块级实验的广泛测试表明，SGF在长视频外推方面相较于自我强制方法表现更强，尤其在主体一致性、背景/布局一致性及时序稳定性方面。值得注意的是，仅使用5秒的训练窗口，SGF即可外推生成持续数分钟的视频。我们将公开代码与模型，以推动自回归视频生成的研究。

English

Recent autoregressive video diffusion methods are increasingly built upon Self Forcing, where the student is trained on histories produced by its own rollout rather than ground-truth video contexts. This reduces exposure bias, but the historical key-value cache is still used by future frames only as frozen rollout state. As a result, future losses cannot supervise how earlier generated latents should be written into more useful keys and values for later video-latent generation. We call this the historical context-gradient gap. We propose Self Gradient Forcing (SGF), a two-pass training strategy that restores this missing supervision signal without backpropagating through the full serial rollout. Pass 1 performs a no-gradient autoregressive rollout matching inference and, at a sampled denoising exit step, records both the self-generated context and the noisy latents fed to the model. Pass 2 performs parallel context-gradient reconstruction for the recorded exit step. The generated context is used as stop-gradient clean-latent input, while the model recomputes the context KV representations and future-to-context causal attention. Thus, SGF provides the missing memory-writing supervision within the native autoregressive training objective, using losses on future video latents to train the model to encode context into more effective causal memory. Across extensive long-horizon frame-wise and chunk-wise experiments under different initializations, SGF achieves stronger native long-video extrapolation than Self Forcing, especially in subject identity, background/layout consistency, and temporal stability. Remarkably, using only a 5-second training window, SGF can extrapolate to videos lasting several minutes. Code and models will be released to advance research on autoregressive video generation.