An arXiv paper (arXiv:2610.20220, submitted 14 Oct 2026) proposes Glance Forcing, a lightweight distillation framework that converts bidirectional diffusion video models into causal few-step autoregressive students. The authors say existing pipelines rely on offline Probability Flow ODE trajectories costing tens of thousands of GPU-hours and terabytes of storage, and that reducing supervision to a single sample causes quality collapse traced to differing curvature in the high-noise and low-noise regimes. Glance Forcing instead decouples the denoising trajectory into slow and fast experts with stage-specific objectives while keeping the pretrained teacher frozen; the paper reports that with only one PF-ODE trajectory it raises VBench Total from 84.04 to 84.22, cuts trajectory-collection cost from thousands of GPU-hours to about one A100 GPU-hour, and shows larger gains in dynamic degree, VisionReward and instruction-following, with human raters consistently preferring it over strong baselines.