Accelerating World Action Models via Parallel Sampling

World Action Models (WAMs) offer a promising approach to robot learning, but iterative sampling incurs substantial inference latency, limiting closed-loop responsiveness. Fast samplers and caching methods reduce the number or cost of model evaluations but typically retain sequential sampling dependencies. We present DASH, a training-free framework that accelerates pretrained WAM sampling through parallel trajectory refinement to reduce sequential execution depth. DASH initializes new denoising states by extrapolating from model predictions at the current anchor and refines multiple positions concurrently through Picard iteration. To determine window advancement, it estimates how residual inconsistencies in video and action states contribute to deviations in the generated action chunk, accepting a contiguous prefix that satisfies a cumulative contribution budget at each position. Our approach achieves $1.71-2.66\times$ wall-clock speedups over sequential sampling across multiple WAMs on RoboTwin 2.0 while maintaining comparable task performance. Evaluation on three real-world manipulation tasks of increasing difficulty further demonstrates a better performance-latency tradeoff than the evaluated baselines.