Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs
Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by the high latency of full inference. We propose Realtime-VLA FLASH, a speculative inference framework that eliminates most full inference calls during replanning by introducing a lightweight draft model with parallel verification via the main model's Action Expert and a phase-aware fallback mechanism that reverts to the full inference pipeline when needed. This design enables low-latency, high-frequency replanning without sacrificing reliability. Experiments show that on LIBERO, FLASH largely preserves task performance by replacing many 58.0...
| Authors | Jiahui Niu, Kefan Gu, Yucheng Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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