Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation

Interactive streaming video generation requires a causal model to react to evolving event conditions while preserving the visual trajectory it has already established. 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 typically distill high-quality bidirectional diffusion models into autoregressive generators and further adapt them via streaming long-tuning; however, they often suffer from identity, layout, and scene drift when event switches. We identify the cause of this failure as conditional bias, where the teacher can produce denoising guidance that is semantically valid for the new event but incompatible with the student’s realized history, thereby causing distribution matching to propagate prompt-consistent yet trajectory-inconsistent modes. We propose Delta Forcing, a transition-calibrated steering objective for a distribution-matching distillation. For each generated chunk, Delta Forcing compares the feature-space delta of the teacher’s denoised estimate with the delta of the generator’s history-grounded rollout, retaining full teacherdriven distribution matching when the transitions agree and shifting toward trajectory-continuity regularization when they diverge. This adaptive supervision restores the balance between reactivity and stability, enabling coherent long-term generation under dynamic conditions. Extensive experiments demonstrate that Delta Forcing improves long-horizon stability while maintaining responsiveness to evolving events.