Distributed RL

RL: Reinforcement Learning

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning
Sep 21, 2026Xuanlin Jiang, Samuel Hsia, Michael Kuchnik +3RL for Language Models Distributed RL
FAIR at Meta · Harvard University · Work carried out at Meta
Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
Sep 2, 2026Pritthijit Nath, Sebastian Schemm, Peter Haynes +2Weather Forecasting Distributed RL
Department of Applied Mathematics and Theoretical Physics, University of Cambridge · Department of Computer Science and Technology, University of Cambridge · Met Office Hadley Centre
Variance-Reduced Q-Learning over Static and Time-Varying Networks
Jul 24, 2026Sreejeet Maity, Feng Zhu, Aritra Mitra +1Reinforcement Learning Q-Learning
Department of Electrical and Computer Engineering, North Carolina State University · Department of Electrical and Computer Engineering, University of California San Diego, San Diego, USA
Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning
Jul 22, 2026Jian Hu, Huiying Li, Hao Zhang +8Agentic RL Reinforcement Learning
NVIDIA
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
Jun 23, 2026Sijie Wang, Zhengyu Qing, Zhiqiang Tan +6Reinforcement Learning Distributed RL
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou) · Huawei Technologies Ltd.
AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning
Jun 3, 2026Qingxu Fu, Boyin Liu, Shuchang Tao +2Multi-Agent LLM Systems Agentic RL
Tongyi Lab, Alibaba Group
Libra: Efficient Resource Management for Agentic RL Post-Training
Jun 2, 2026Kaiwen Chen, Xin Tan, Jingzong Li +6Agentic RL Reinforcement Learning
The Chinese University of Hong Kong · The Hang Seng University of Hong Kong · Independent Researcher
Darwin Mobile Agent: A Roadmap for Self-Evolution
May 26, 2026Daniel Beechey, Derek Yuen, Jianheng Liu +5Reinforcement Learning RL for GUI Agents
1Huawei Noah’s Ark Lab · University College London
SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication
May 8, 2026Lucas Hu, Ranchi Zhao, Isaac Zhu +4Reinforcement Learning Communication-Efficient Distributed Training
Scitix
RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI
Feb 8, 2026Hongzhi Zang, Shu'ang Yu, Hao Lin +14Robot Policy Learning Reinforcement Learning
1Tsinghua University · 6Shanghai AI Laboratory · 2Infinigence AI +3
AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training
Date pendingZhenyu Han, Ansheng You, Haibo Wang +16RL for Language Models Asynchronous RL
Huawei · Individual Researcher

Sep 21, 2026, cs.DCJ/K move · Enter open · S save

WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning

Xuanlin Jiang, Samuel Hsia, Michael Kuchnik, Zachary DeVito+2

FAIR at Meta · Harvard University · Work carried out at Meta

RL for Language Models Distributed RL

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Weight transfer - the propagation of updated parameters from trainers to rollout generators - is becoming an important performance bottleneck in reinforcement learning (RL) systems for LLMs. The central challenge is supporting the diverse trainer and rollout layouts and synchronization requirements of modern RL workloads without sacrificing efficiency. Existing solutions are efficient under some configurations but perform poorly or lack support under others. We present WeightBridge, a flexible, efficient weight-transfer library designed to deliver high performance across diverse RL configurations. WeightBridge first automatically extracts the correspondence between trainer and rollout weight layouts, then plans and executes redundancy-free and load-balanced weight transfer. It exposes a small, general API while coordinating workers across diverse synchronization modes. Across configurations spanning different models, parallelization layouts, and synchronization modes, WeightBridge reduces average GPU stall time by up to 42

\times

over the state-of-the-art open-source RL framework and achieves high performance in all settings. A coding agent was able to integrate WeightBridge into two different RL frameworks without manual guidance, demonstrating the generality and ease of use of its APIs.

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Latest papers 11