# Realtime-VLA V2: Learning to Run VLAs Fast, Smooth, and Accurate

arXiv: https://arxiv.org/abs/2603.26360 · HTML: https://mlsanity.com/paper/2603.26360/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2026.

## Built on by (most cited first, 8 of 8)

- [FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking](https://mlsanity.com/paper/2607.24008/) (2026)
- [Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs](https://mlsanity.com/paper/2605.13778/) (2026)
- [FLARE: Diffusion for Hybrid Language Model](https://mlsanity.com/paper/2606.01774/) (2026)
- [DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model](https://mlsanity.com/paper/2606.12105/) (2026)
- [TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies](https://mlsanity.com/paper/2606.06491/) (2026)
- [Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference](https://mlsanity.com/paper/2607.12659/) (2026)
- [GROOVE: Geometry-Guided Reduction of Operational-Space Jerk in VLA Execution](https://mlsanity.com/paper/2609.13695/) (2026)
- [Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation](https://mlsanity.com/paper/2609.39822/) (2026)

## Abstract (arXiv)

In deployment of the VLA models to real-world robotic tasks, execution speed matters. In previous work arXiv:2510.26742 we analyze how to make neural computation of VLAs on GPU fast. However, we leave the question of how to actually deploy the VLA system on the real robots open. In this report we describe a set of practical techniques to achieve the end-to-end result of running a VLA-driven robot at an impressive speed in real world tasks that require both accuracy and dexterity. The stack of technology ranges across calibration, planning & control, and learning based method to identify optimal execution speed. In the tasks we show, the robot even executes in a speed on par with casual human operation and approaching the hardware limit of our lightweight arm. The unaccelerated videos and inference traces are provided in https://dexmal.github.io/realtime-vla-v2/.