RL-VLA$^3$ is a fully asynchronous distributed reinforcement learning framework designed for Vision‑Language‑Action (VLA) model training. It allows fine‑grained asynchronous interaction between simulation, inference, and training via dynamic batching schedulers and flexible environment sharding, addressing the variable, resource‑intensive latencies of physical simulators. Experiments across multiple simulation backends, VLA architectures, and RL algorithms show throughput gains of up to 85.2% over synchronous baselines while preserving sample efficiency, and the system scales from 8 to 256 GPUs.
By Haoran Sun, Yongjian Guo, Zhong Guan, Shuai Di, Xiaodong Bai, Jing Long, Tianyun Zhao, Mingxi Luo, Hongke Zhao, Likang Wu, Xiaotie Deng, Xu Chu, Xi Xiao, Sheng Wen, Yicheng Gong, Junwu Xiong
arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.
By Wei Gao, Yuheng Zhao, Tianyuan Wu, Shaopan Xiong, Weixun Wang, Dakai An, Lunxi Cao, Dilxat Muhtar, Zichen Liu, Haizhou Zhao, Ju Huang, Siran Yang, Yongbin Li, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng, Wei Wang
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
By Kaiwen Chen, Xin Tan, Jingzong Li, Hong Xu
arXiv:2609.24456v1 Announce Type: cross
Abstract: Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, howev...
By Sitong Zhang, Tuo Shi, Mario Di Francesco, Zeke Wang, Bo Zhao
arXiv:2608. 10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.
By Yanyu Ren, Xizheng Wang, Xiao Liu, Bowen Lv, Hanchen Zhang, Shudan Zhang, Hanyu Lai, Shuai Wang, Li Chen, Dan Li, Jie Tang
arXiv:2608. 14498v1 Announce Type: new Abstract: Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions.
By Hanfeng Lu, Tianyu Feng, Suyi Li, Yuheng Zhao, Wei Gao, Shaopan Xiong, Ju Huang, Siran Yang, Jiamang Wang, Lin Qu, Wei Wang
arXiv:2608.22167v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL framework...
By Ziyang Luo, Yan Yang, Xiangru Jian, Ziji Shi, Xiaoqiang Lin, Jun Hao Liew, Silvio Savarese, Junnan Li
FluxVLA Engine is an open, configuration‑driven platform that unifies the fragmented components of embodied policy development—datasets, visual‑language and world models, action heads, learning methods, distributed training, simulation evaluation, inference, and robot interfaces—into a reproducible data‑to‑deployment workflow. It adds features such as compositional dual‑arm simulation, scalable automatic data generation, human‑in‑the‑loop rollout and correction, Real‑Time Chunking for fast inference, and lightweight remote GPU serving, thereby linking offline learning, simulation validation, online correction, and real‑robot execution under shared, auditable contracts. The engine aims to eliminate engineering bottlenecks that currently separate promising embodied‑learning algorithms from reliable, reproducible deployment.
By Yinhao Li, Weixin Mao, Zihan Lan, Jikun Rong, Qirui Hu, Yiming Zhang, Weipeng Deng, Bowen Shen, Minzhao Zhu, Yiming Mao, Yan Yang, Chenguang Cui, Hongyuan Chen, Xu Huang, Zheyi Zhao, Pinxi Shen, Bozhen He, Zhen Fu, Yifan Wang, Zexin Zhang, Ang Gao, Haoyu Chen, Chengqi Shi, Hua Chen
arXiv:2609.08368v1 Announce Type: new
Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each st...
By RadixArk, :, Tom Chen, Mao Cheng, Shi Dong, Kangrui Du, Yanbin Jiang, Jiajun Li, Yiming Li, Tao Lin, Yusheng Su, Andy Ye, Yueming Yuan, Zhichen Zeng
arXiv:2608. 06025v1 Announce Type: new Abstract: In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations.
By Jiming Su, Hantao Hua, Lujia Yin, Yiping Yao, Feng Zhu
WeightBridge is a lightweight library that streamlines weight transfer between trainers and rollout generators in reinforcement learning systems, particularly for large language models. It automatically maps trainer and rollout weight layouts, then performs redundancy‑free, load‑balanced transfers while supporting various synchronization modes. Experiments show that WeightBridge can cut GPU stall time by up to 42× compared to leading open‑source RL frameworks, and it was easily integrated into two different frameworks by a coding agent.
By Xuanlin Jiang, Samuel Hsia, Michael Kuchnik, Zachary DeVito, Minlan Yu, Carole-Jean Wu
arXiv:2607. 17299v1 Announce Type: cross Abstract: Long-horizon rollout generation has become the dominant systems bottleneck in agentic reinforcement learning (RL).
By Ryan Xu, Atlas Zhao, David Bao, Frank Du