State Forcing: Telic Instruction Following for Streaming Video Generation

Recent advances in autoregressive video generation have enabled efficient streaming beyond the native clip lengths of pretrained models. Yet visually coherent rollouts can still exhibit progress regression and re-execution of completed actions. We study telic instruction following, which requires completing a requested state transition without regression and preserving its consequence. Our empirical study shows that verified terminal states can guide progress toward completion, and support consequence retention in a frozen generator. Building on this finding, we introduce State Forcing, a state-privileged on-policy self-distillation (OPSD) framework. Through complementary Advance and Hold conditions, a frozen teacher uses offline terminal states to supervise progress and consequence retention along student-generated trajectories. The student retains the original instruction and streaming pipeline, without additional inputs or computation at inference. We also introduce TelicBench to jointly evaluate completion and temporal validity over full rollouts. State Forcing outperforms the evaluated baselines in completion and strict success, achieving 37.33% strict success compared with 24.93% for the strongest baseline. These gains are achieved with overall video quality comparable to the distilled streaming baselines.