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Parallel Stochastic Gradientbased Planning For World Models

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Yann LeCun
paper · 2026-01-31
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We provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gradient-based optimization (GD) on long-horizon experiments, both in success rate and time to convergence.

GRASP: A Parallel Stochastic Gradient Planner for World Models