arXiv:2511.14148v1 Announce Type: cross 
Abstract: Vision-language-action (VLA) models have recently emerged as a powerful paradigm for building generalist robots. However, traditional VLA models that generate actions through flow matching (FM) typically rely on rigid and uniform time schedules, i.e., synchronous FM (SFM). Without action context awareness and asynchronous self-correction, SFM becomes unstable in long-horizon tasks, where a single action error can cascade into failure. In this work, we propose asynchronous flow matching VLA (AsyncVLA), a novel framework that introduces temporal flexibility in asynchronous FM (AFM) and enables self-correction in action generation. AsyncVLA breaks from the vanilla SFM in VLA models by generating the action tokens in a non-uniform time schedule with action context awareness. Besides, our method introduces the confidence rater to extract confidence of the initially generated actions, enabling the model to selectively refine inaccurate action tokens before execution. Moreover, we propose a unified training procedure for SFM and AFM that endows a single model with both modes, improving KV-cache utilization. Extensive experiments on robotic manipulation benchmarks demonstrate that AsyncVLA is data-efficient and exhibits self-correction ability. AsyncVLA achieves state-of-the-art results across general embodied evaluations due to its asynchronous generation in AFM. Our code is available at https://github.com/YuhuaJiang2002/AsyncVLA.

AsyncVLA هو إطار جديد لنماذج رؤية-لغة-عمل (VLA) يعالج قيود نماذج المطابقة المتزامنة (SFM). غالبًا ما تؤدي SFM إلى عدم الاستقرار في المهام طويلة الأمد بسبب الجداول الزمنية الصارمة ونقص الوعي بسياق العمل. يقدم AsyncVLA مطابقة تدفق غير متزامنة (AFM)، مما يسمح بالمرونة الزمنية والتصحيح الذاتي في توليد العمل. تعزز هذه الابتكار قدرة النموذج على تحسين الأعمال بناءً على تقييمات الثقة، مما يحسن الأداء العام في التطبيقات الروبوتية.

AsyncVLA es un nuevo marco para los modelos de Acción-Lenguaje-Visión (VLA) que aborda las limitaciones de los modelos de coincidencia de flujo sincrónicos (SFM). El SFM a menudo conduce a inestabilidad en tareas a largo plazo debido a horarios rígidos y falta de conciencia contextual de las acciones. AsyncVLA introduce la coincidencia de flujo asincrónica (AFM), permitiendo flexibilidad temporal y autocorrección en la generación de acciones. Esta innovación mejora la capacidad del modelo para refinar acciones basadas en calificaciones de confianza, mejorando el rendimiento general en aplicaci…

AsyncVLA est un nouveau cadre pour les modèles Vision-Langage-Action (VLA) qui répond aux limites des modèles de correspondance de flux synchrones (SFM). Le SFM entraîne souvent une instabilité dans les tâches à long terme en raison de calendriers rigides et d'un manque de conscience contextuelle des actions. AsyncVLA introduit la correspondance de flux asynchrone (AFM), permettant une flexibilité temporelle et une autocorrection dans la génération d'actions. Cette innovation améliore la capacité du modèle à affiner les actions en fonction des évaluations de confiance, améliorant ainsi les per…

AsyncVLA is a new framework for Vision-Language-Action (VLA) models that addresses the limitations of traditional synchronous flow matching (SFM). SFM often leads to instability in long-horizon tasks due to rigid time schedules and lack of action context awareness. AsyncVLA introduces asynchronous flow matching (AFM), allowing for temporal flexibility and self-correction in action generation. This innovation enhances the model's ability to refine actions based on confidence ratings, improving overall performance in robotic applications.

AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

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