Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models

2026-06-24

AI Summary

This work addresses the inefficiency and suboptimal generation quality of autoregressive video diffusion models in streaming generation and action-conditioned interactive world modeling. To overcome these limitations, the authors propose a unified causal diffusion distillation framework that integrates teacher forcing and self-forcing strategies, and—critically—introduces continuous-time rectified consistency models (rCM) into autoregressive video generation for the first time. The resulting approach, termed Causal-rCM, leverages a causal diffusion Transformer, distribution matching distillation (DMD), and a custom masked FlashAttention-2 Jacobian-vector product kernel to enable scalable and efficient training. Remarkably, the 2-step sampling Wan2.1-1.3B model, trained solely on synthetic data, achieves a score of 84.63 on VBench-T2V, substantially outperforming existing methods, and has been successfully integrated into Cosmos 3 for high-efficiency interactive world modeling.

Abstract

Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-conditioned interactive world models. In this work, we extend rCM, an advanced diffusion distillation framework, to autoregressive video diffusion. The core philosophy of rCM lies in the complementarity between forward and reverse divergences, represented by consistency models (CMs) and distribution matching distillation (DMD), respectively, in diffusion distillation. This philosophy naturally carries over to the autoregressive setting, where teacher-forcing (TF) provides an offline, forward-divergence causal training paradigm, while self-forcing (SF) corresponds to an on-policy, reverse-divergence refinement. Our contributions are: (1) through extensive experiments, we show that teacher-forcing CM is currently the best complement to self-forcing DMD as an initialization strategy (2) we present the first implementation of teacher-forcing-based continuous-time CMs (e.g., sCM/MeanFlow) for autoregressive video diffusion, enabled by our custom-mask FlashAttention-2 JVP kernel, achieving 10$\times$ faster convergence compared to discrete-time CMs (dCMs) (3) we introduce Causal-rCM, a leading, unified, and scalable algorithm-infrastructure open recipe for diffusion distillation and causal training (4) we achieve state-of-the-art streaming video generation performance in both frame-wise and chunk-wise settings, using only synthetic data for training. Notably, our distilled 2-step causal Wan2.1-1.3B model achieves a VBench-T2V score of 84.63 with only 1 or 2 sampling steps. We further apply Causal-rCM to Cosmos 3, an advanced omnimodal world foundation model for physical AI with action-conditioned generation capability, enabling an interactive world model.

Problem

• autoregressive video diffusion
• streaming video generation
• interactive world models
• diffusion distillation
• causal training

Innovation

• Causal-rCM
• autoregressive diffusion
• teacher-forcing
• consistency models
• diffusion distillation