This paper presents Speedup Patch (SuP), a lightweight policy-agnostic framework designed to accelerate embodied manipulation by adaptively downsampling action chunks from existing policies. The method formulates the scheduler optimization as a Constrained Markov Decision Process to maximize efficiency while maintaining task performance. To address offline safety constraints, the approach introduces World Model based state deviation as a surrogate metric for success evaluation. SuP demonstrates that embodied manipulation tasks can be significantly accelerated without requiring policy retraining or costly online interactions.