Next Forcing: Causal World Modeling with Multi-Chunk Prediction

Hugging Face Daily Papers Papers
autoregressive-video-generation causal-world-modeling multi-chunk-prediction video-generation inference-acceleration

Summary

Next Forcing introduces a multi-chunk prediction framework for causal world modeling that accelerates training and inference for autoregressive video generation while improving accuracy and physical law adherence.

Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterative video denoising. 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, Next Forcing achieves a 93.1% relative improvement over LingBot-VA at 5k training steps and 2.3x faster convergence, and establishes new state-of-the-art results on the RoboTwin benchmark (94.1/93.5% on Clean/Random). At inference, the MCP modules can be retained to predict the next video chunk in parallel with the current one, achieving 2x inference acceleration. Next Forcing also demonstrates significant improvements on PhyWorld, a benchmark evaluating adherence to physical laws in video generation, and over 50% FVD reduction on general video pretraining.
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Source: https://huggingface.co/papers/2606.11187

Abstract

Next Forcing introduces a multi-chunk prediction framework that accelerates training and inference for autoregressive video generation while improving accuracy and physical law adherence.

Autoregressive video generationhas emerged as a powerful paradigm forWorld Action Models(WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterativevideo denoising. In this paper, we present Next Forcing, amulti-chunk prediction(MCP) framework forcausal world modelingthat 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 futuretemporal horizons(next^1, next^2, next^3 chunks). These MCP modules form acausal chainacross 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 densemulti-scale temporal supervisionback to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates: at 50 fps, Next Forcing achieves a 93.1% relative improvement over LingBot-VA at 5k training steps and 2.3x faster convergence, and establishes new state-of-the-art results on the RoboTwin benchmark (94.1/93.5% on Clean/Random). At inference, the MCP modules can be retained to predict the next video chunk in parallel with the current one, achieving 2xinference acceleration. Next Forcing also demonstrates significant improvements onPhyWorld, a benchmark evaluating adherence to physical laws in video generation, and over 50%FVDreduction on general video pretraining.

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