Action-Chunk Pruning: Learning an Efficient Execution Scheduler for Embodied Policies

Embodied policies trained from expert demonstrations have achieved strong performance in many tasks. However, since imitation learning fundamentally binds policy behavior to demonstration quality, slow or overly cautious movements in human demonstrations often lead the learned policy to adopt corresponding behavioral patterns, resulting in low execution efficiency. Existing approaches typically address this issue through dataset refinement followed by policy retraining or additional online interaction, both of which incur high computational and data-collection costs. In this work, we introduce **A**ction-**C**hunk **P**runing (**ACP**), a lightweight,offline framework that enables **plug-and-play policy acceleration**. ACP formulates policy acceleration as an action-chunk pruning problem, aiming to accelerate execution by replacing redundant action segments with feasible shortcuts while preserving the original motion. Specifically, we use a learned world model to evaluate candidate action shortcuts on offline demonstrations and construct a shortcut Directed Acyclic Graph (DAG), where Dynamic Programming (DP) computes optimal next-hop decisions starting from each state to minimize the remaining number of execution steps. These pruning decisions are then amortized into a lightweight scheduler that predicts a stride for each position in the action chunk in a single forward pass, avoiding repeated world-model evaluation and online optimization. We demonstrate ACP in simulation and on real-world tasks, validating its capability to accelerate execution while preserving the base policy's performance.