# VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots arXiv: https://arxiv.org/abs/2512.24673 · HTML: https://mlsanity.com/paper/2512.24673/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Placed in Efficient VLA inference and adaptation, one of 13 directions in Vision-language-action models. - Builds on 20 of its 25 references that are in the map. - First posted on arXiv 31 Dec 2025. ## Placed in - Vision-language-action models / [Efficient VLA inference and adaptation](https://mlsanity.com/vla/efficient-vla-inference-and-adaptation/) ## Builds on (most cited first, 20 of 20) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024) - [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025) - [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023) - [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024) - [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025) - [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025) - [A Survey on Model Compression for Large Language Models](https://mlsanity.com/paper/2308.07633/) (2023) - [RVT: Robotic View Transformer for 3D Object Manipulation](https://mlsanity.com/paper/2306.14896/) (2023) - [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025) - [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024) - [A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends](https://mlsanity.com/paper/2407.07403/) (2024) - [Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges](https://mlsanity.com/paper/2505.04769/) (2025) - [VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference](https://mlsanity.com/paper/2512.01031/) (2025) - [Leave No Observation Behind: Real-time Correction for VLA Action Chunks](https://mlsanity.com/paper/2509.23224/) (2025) - [Running VLAs at Real-time Speed](https://mlsanity.com/paper/2510.26742/) (2025) - [Teleoperation of Humanoid Robots: A Survey](https://mlsanity.com/paper/2301.04317/) (2023) ## Built on by (most cited first, 19 of 19) - [FASTER: Rethinking Real-Time Flow VLAs](https://mlsanity.com/paper/2603.19199/) (2026) - [AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge](https://mlsanity.com/paper/2602.13476/) (2026) - [GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization](https://mlsanity.com/paper/2605.12369/) (2026) - [DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model](https://mlsanity.com/paper/2606.12105/) (2026) - [OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism](https://mlsanity.com/paper/2603.14371/) (2026) - [See, Remember, Explore: A Benchmark and Baselines for Streaming Spatial Reasoning](https://mlsanity.com/paper/2603.23864/) (2026) - [Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference](https://mlsanity.com/paper/2607.12659/) (2026) - [Acting While Understanding: Asynchronous Semantic-Action Decoupling for Real-Time Vision-Language-Action Models](https://mlsanity.com/paper/2606.15285/) (2026) - [Hermite Curves as Trajectory Priors for Vision-Language-Action Models](https://mlsanity.com/paper/2608.01265/) (2026) - [RAPAC-DP: Response-Aligned Pending-Action Compensation for Diffusion Policies under Delayed Execution](https://mlsanity.com/paper/2608.15924/) (2026) - [NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation](https://mlsanity.com/paper/2608.16503/) (2026) - [TAO-Force: Unifying Force-Aware Perception and Fast-Slow Control for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.18497/) (2026) - [TAO-DA: Towards Autonomous Operation--A Dual-Arm Vision-Language-Action Model for Coordinated Manipulation](https://mlsanity.com/paper/2609.33197/) (2026) - [RAVEL: Asynchronous Rolling Inference for Flow-Based Vision-Language-Action Models](https://mlsanity.com/paper/2609.34170/) (2026) - [Faster and Better? Benchmark Bugs and Design Limitations Distort the Evaluation of Vision-Language-Action Acceleration](https://mlsanity.com/paper/2609.37771/) (2026) - [Online Evolution Strategy for Flow-Matching VLA Policies via Self-Supervised Trajectory Distribution Optimization](https://mlsanity.com/paper/2609.38855/) (2026) - [DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction](https://mlsanity.com/paper/2609.39198/) (2026) - [REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models](https://mlsanity.com/paper/2610.12007/) (2026) - [JitterVLA: A Replay Benchmark for Motion Quality in VLA-Driven Human-Robot Manipulation](https://mlsanity.com/paper/2610.13628/) (2026) ## Abstract (arXiv) Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing methods suffer from jitter, stalling, or even pauses in robotic action execution, which not only limits the achievable execution speed but also reduces the overall success rate of task completion. This paper introduces VLA-RAIL (A Real-Time Asynchronous Inference Linker), a novel framework designed to address these issues by conducting model inference and robot motion control asynchronously and guaranteeing smooth, continuous, and high-speed action execution. The core contributions of the paper are two fold: a Trajectory Smoother that effectively filters out the noise and jitter in the trajectory of one action chunk using polynomial fitting and a Chunk Fuser that seamlessly align the current executing trajectory and the newly arrived chunk, ensuring position, velocity, and acceleration continuity between two successive action chunks. We validate the effectiveness of VLA-RAIL on a benchmark of dynamic simulation tasks and several real-world manipulation tasks. Experimental results demonstrate that VLA-RAIL significantly reduces motion jitter, enhances execution speed, and improves task success rates, which will become a key infrastructure for the large-scale deployment of VLA models.