Why Plan From Scratch? Accelerating Diffusion VLAs Via Trajectory Recycling

Diffusion-based Vision-Language-Action (dVLA) models are a powerful paradigm for continuous robotic control, but their real-world deployment is bottlenecked by slow inference driven by iterative diffusion processes and massive multimodal backbones. Current acceleration methods naively discard unexecuted tail-end actions during dynamic re-planning. This squanders prior computational effort and repeatedly invokes the heavy dVLA backbone, bounding achievable latency reduction. We propose WPFS (Why Plan From Scratch?), a framework that accelerates dVLAs by repurposing discarded action chunks as structural priors. WPFS introduces a specialized Corrector that refines these priors into valid trajectories using only a fraction of the original denoising steps. We instantiate two variants: WPFS-Large, which leverages the base model for refinement, and WPFS-Small, which utilizes a distilled model to bypass the massive perceptual backbone entirely. To ensure reliability, an uncertainty-aware self-verification mechanism quantifies decision disagreement in parallel, dynamically triggering a full-scale fallback in complex scenarios without brittle threshold tuning. Evaluated across state-of-the-art dVLAs on simulation benchmarks (LIBERO, SimplerEnv) and real-world robots, WPFS achieves extreme latency reductions while maintaining or improving task success rates and motion smoothness. Code is available at https://anonymous.4open.science/r/WPFS-FFDD.