Next Forcing: Causal World Modeling with Multi-Chunk Prediction

Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence particularly at high frame rates, limited converged accuracy, and slow inference due to iterative video denoising, as the training supervision is confined to the current chunk without explicit signals about future dynamics. In this paper, we present Next Forcing, a multi-chunk prediction (MCP) framework for causal world modeling that enables faster training, higher accuracy, and accelerated inference. Inspired by multi-token prediction in large language models, Next Forcing introduces an MCP training objective that augments the main model with lightweight auxiliary MCP modules to simultaneously denoise video chunks at multiple future temporal horizons (next$^1$, next$^2$, next$^3$ chunks). These MCP modules form a causal chain across prediction depths, where intermediate features fused from multiple layers of the main model are leveraged to predict future dynamics, allowing near-future predictions to inform farther-future ones and providing dense multi-scale temporal supervision back to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates. At 50 fps, our method achieves a 93.1\% relative improvement over the baseline LingBot-VA at 5k training steps and achieves 2.3$\times$ faster convergence. At inference, the MCP modules can be retained to predict the next video chunk in parallel with the current one, accelerating generation. Next Forcing establishes new state-of-the-art results on the RoboTwin benchmark (94.1\%/93.5\% on Clean/Random) and demonstrates significant improvements on PhyWorld, a benchmark evaluating adherence to physical laws in video generation.