Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance

Vision–Language–Action (VLA) models have become a com-pelling route toward robotic foundation models by mapping multimodalperception and language instructions directly into executable actions. Be-yond the prevalent flow-matching VLAs, discrete diffusion VLA (dVLA)models further unify perception and control in a single discrete space,offering inherent advantages in unified multimodal alignment and under-standing, while better preserving the prior knowledge of vision-languagemodels. Yet this strength comes with a practical obstacle that inferenceis far too slow for real-time control, as the parallel decoding prevents theutilization of Key-Value (KV) cache. In this paper, we aim to tackle thischallenge by leveraging an intriguing observation that the dVLA withbidirectional attention still adheres to a block-wise left-to-right order,which motivates the application of block diffusion. However, directlyapplying block diffusion precludes inter-block parallelism, a key accel-eration factor. To address the limitation, we introduce Fast-dVLA, anblock-wise diffusion acceleration strategy. Fast-dVLA takes the full actiontoken sequence at each timestep as an action block and denoises themtogether. It then decodes different blocks in an autoregressive mannerto allow KV cache reusing, while allowing inter-block parallel decodingin a diffusion-forcing manner. For efficient training, we directly conductan asymmetric distillation on a finetuned dVLA. Extensive evaluationson Discrete Diffusion VLA, Dream-VLA, and UD-VLA across diversesimulated benchmarks validate that our Fast-dVLA achieves 2.8×-4.1×speedup and maintains state-of-the-art performance. Moreover, the resultson real-world high-dynamics tasks demonstrate the potential of real-timedeployment and application of our Fast-dVLA.