Fast LeWorldModel
Summary
Fast-LeWM accelerates visual planning by replacing autoregressive rollout with parallel action-prefix prediction, reducing computational costs and latency accumulation during long-horizon predictions.
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Paper page - Fast LeWorldModel
Source: https://huggingface.co/papers/2606.26217
Abstract
Fast-LeWM accelerates visual planning by replacing autoregressive rollout with parallel action-prefix prediction, reducing computational costs and latency accumulation during long-horizon predictions.
Joint-Embedding Predictive Architectures(JEPAs), including recentLeWorldModel(LeWM), have become a promising foundation for reconstruction-free visual world models. Forvisual planning, however, LeWM evaluates candidate action sequences by repeatedly applying a local one-steplatent transition model. Thisautoregressive rolloutmakes planning computationally expensive and exposes the predicted trajectory to accumulated latent errors as the horizon grows. We propose FastLeWorldModel(Fast-LeWM), a fastlatent world modelthat replaces repeated local rollout withaction-prefix prediction. Given the current latent and a candidate action sequence, Fast-LeWM encodes its prefixes and predicts the future latents reached after executing those prefixes in parallel. By making action prefixes the basic prediction unit, Fast-LeWM directly models action effects accumulated to different extents over multiple horizons. Thisprefix-level supervisionforces the model to learn how states continuously evolve under different action prefixes, rather than only fitting one-step state transitions. During planning, the predictor can use the last prefix token from the encoded action sequence to evaluate the corresponding future latent without explicitly rolling through each intermediate imagined state. Across multiple tasks, Fast-LeWM improves average success over LeWM while substantially reducing planning time, achieving loweropen-loop latent losswhose growth becomes significantly slower as the rollout horizon increases.
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