DASH: Difficulty-Aware and Maturity-Oriented Streaming for Real-Time Flow-Based VLAs

Flow-based Vision-Language-Action (VLA) models generate continuous robot actions through iterative denoising, but this process creates a latency–quality trade-off for real-time control. Recent studies adopt asynchronous and streaming execution schemes to reduce Time to First Action (TTFA) by overlapping generation with execution or releasing early action tokens before the full action chunk is denoised. However, we find that early-released actions must remain reliable within each chunk, while denoising and observation frequency should be coordinated according to sample-level convergence difficulty of actions. To this end, we propose DASH (Difficulty-Aware Maturity-Oriented Streaming), which combines Maturity-Oriented Matching to improve causal consistency within each action chunk with Difficulty-Aware Denoising to jointly adapt denoising budgets and observation frequency based on convergence difficulty, thereby enhancing both action reliability and closed-loop responsiveness. Our simulation and real-world experiments demonstrate up to 3.44$\times$ reaction speedup over synchronous inference and up to 3.26% improvement in average CALVIN success over the strongest streaming baseline.