Stabilizing Long-Horizon Streaming DMD with Drift-Controlled Adaptive TTUR

Distribution matching distillation (DMD) relies on an online fake-score model that tracks an evolving generator distribution; tracking errors can perturb generator updates. Fixed two-timescale update rules (TTUR) use constant critic-to-generator ratios that can be poorly suited to the varying tracking burden in long-horizon streaming DMD. We introduce Drift-Gated TTUR, which adapts only optimizer timing. A periodic fixed-paired probe holds prompts, latents, diffusion timesteps, and forward noise fixed while measuring temporal changes in the on-policy fake- score field as a one-sided indicator of elevated change in the coupled generator– critic dynamics. After a short calibration, exceeding a frozen threshold triggers a skipped generator update and additional critic recovery; otherwise, the original schedule is retained. DMD objectives and inference remain unchanged, with no gradient through the probe or controller. On the official 946-prompt VBench evaluation in LongLive, our method achieves Total and Quality scores of 81.05 and 83.59, compared with 80.16 and 82.93 for the uniform-schedule control. The Total gain is +0.89 with a paired 95% confidence interval of [+0.60,+1.17] across seven matched inference seeds. A random intervention schedule scores 79.58, while removing critic recovery from the uniform schedule yields 80.03. These controls indicate that neither additional critic optimization nor schedule irregularity alone explains the gain, supporting drift-conditioned optimizer timing as an effective control variable for long-horizon streaming DMD.