[2603.18464] AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models

Computer Science > Machine Learning arXiv:2603.18464 (cs) [Submitted on 19 Mar 2026 (v1), last revised 20 Mar 2026 (this version, v2)] Title:AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models Authors:Chengxuan Lu, Shukuan Wang, Yanjie Li, Wei Liu, Shiji Jin, Fuyuan Qian, Peiming Li, Baigui Sun, Yang Liu View a PDF of the paper titled AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models, by Chengxuan Lu and 8 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models faces significant challenges in computational efficiency and data acquisition. We propose AcceRL, a fully asynchronous and decoupled RL framework designed to eliminate synchronization barriers by physically isolating training, inference, and rollouts. Crucially, AcceRL is the first to integrate a plug-and-play, trainable world model into a distributed asynchronous RL pipeline to generate virtual experiences. Experiments on the LIBERO~\cite{liu2023libero} benchmark demonstrate that AcceRL achieves state-of-the-art (SOTA) performance. Systematically, it exhibits super-linear scaling in throughput and highly efficient hardware utilization. Algorithmically, the world-model-augmented variant delivers unprecedented sample efficiency and robust training stability in complex control tasks. Code is publicly available a...