The paper proposes Representation Forcing, which learns few-step causal initialization for autoregressive video generators through representation-space supervision from a frozen video diffusion transformer instead of intermediate-teacher trajectory regression; the subsequent asymmetric DMD stage is unchanged. The authors report that at four sampling steps it preserves generation quality better than intermediate-teacher-based initialization before DMD, and that after DMD their four-step chunk-wise model improves VBench Total from 84.04 to 84.66 and VisionReward from 6.326 to 10.074 (+59.25%) over the reported causal-ODE-based baseline. The work is posted as arXiv:2610.20111, submitted on 14 Oct 2026 by Siming Fu, Zheming Fu, Lianrui Mu, Haoyang Huang, Chaojie Gu and Ruizhe He.