Closing the Train-Test Gap in World Models for Gradient-Based Planning

Abstract:World models paired with model predictive control (MPC) can be trained offline on large-scale datasets of expert trajectories and enable generalization to a wide range of planning tasks at inference time. Compared to traditional MPC procedures, which rely on slow search algorithms or on iteratively solving optimization problems exactly, gradient-based planning offers a computationally efficient alternative. However, the performance of gradient-based planning has thus far lagged behind that of other approaches. In this paper, we propose improved methods for training world models that enable efficient gradient-based planning. We begin with the observation that although a world model is trained on a next-state prediction objective, it is used at test-time to instead estimate a sequence of actions. The goal of our work is to close this train-test gap. To that end, we propose train-time data synthesis techniques that enable significantly improved gradient-based planning with existing world models. At test time, our approach outperforms or matches the classical gradient-free cross-entropy method (CEM) across a variety of object manipulation and navigation tasks in 10% of the time budget.

Machine Learning (cs.LG); Robotics (cs.RO)

arXiv:2512.09929 [cs.LG]

(or arXiv:2512.09929v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.09929 Focus to learn more arXiv-issued DOI via DataCite

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