How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning

Title:How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning

Abstract:Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservatively large planning budgets. We show that competitive task performance can be achieved without agreement with the Full-budget action, that sufficient budgets vary across model--task pairs, and that iterative planners repeatedly encode solve-invariant context. To address these inefficiencies, we propose {SufficientPlan}, a simple deployment framework that requires no modification to pretrained world models or planner updates. Its {Paired Sequential Budget Certification (PSBC)} component uses paired closed-loop evidence to search for and certify a reduced model--task-specific budget within a predefined Full-performance tolerance. Its {Static-Context Reuse (SCR)} component caches observation and goal representations across search iterations while preserving candidate-dependent planning and selected actions. Experiments across multiple world-model backbones and visual-control tasks show that SufficientPlan substantially reduces search budgets and planning latency while maintaining competitive control performance.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.08350 [cs.RO]

(or arXiv:2610.08350v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.08350 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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