Delta Forcing：交互式自回归视频生成中的信任区域引导
Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation
AI总结：
本文提出Delta Forcing方法，通过约束不可靠的教师监督在适应性信任区域中，以提高自回归视频生成的一致性并保持对新事件的响应性。
摘要：
交互式实时自回归视频生成对于内容创作和世界建模等应用至关重要，其中视觉内容必须适应动态变化的事件条件。一个基本挑战在于在反应性和稳定性之间取得平衡：模型必须迅速响应新事件，同时在长时间范围内保持时间一致性。现有方法将双向模型蒸馏为自回归生成器，并进一步通过流式长调优进行适应，但往往在条件变化后仍会出现持续漂移。我们发现原因在于条件偏差，其中教师可能提供与条件对齐但轨迹无关的指导，使生成偏向于局部有效但全局不一致的模式。受信任区域策略优化的启发，我们提出Delta Forcing，一种简单而有效的框架，它将不可靠的教师监督限制在适应性信任区域内。具体而言，Delta Forcing从教师和生成器轨迹之间的潜在delta估计转移一致性，并利用它来平衡教师监督与单调连续性目标。这抑制了不可靠的教师诱导的偏移，同时保持对新事件的响应性。广泛的实验表明，Delta Forcing在提高一致性的同时保持了事件的响应性。
英文摘要：
Interactive real-time autoregressive video generation is essential for applications such as content creation and world modeling, where visual content must adapt to dynamically evolving event conditions. A fundamental challenge lies in balancing reactivity and stability: models must respond promptly to new events while maintaining temporal coherence over long horizons. Existing approaches distill bidirectional models into autoregressive generators and further adapt them via streaming long tuning, yet often exhibit persistent drift after condition changes. We identify the cause as conditional bias, where the teacher may provide condition-aligned but trajectory-agnostic guidance, biasing generation toward locally valid yet globally inconsistent modes. Inspired by Trust Region Policy Optimization, we propose Delta Forcing, a simple yet effective framework that constrains unreliable teacher supervision within an adaptive trust region. Specifically, Delta Forcing estimates transition consistency from the latent delta between teacher and generator trajectories, and uses it to balance teacher supervision with a monotonic continuity objective. This suppress unreliable teacher-induced shifts while preserving responsiveness to new events. Extensive experiments demonstrate that Delta Forcing significantly improves consistency while maintaining event reactivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
