arXiv Machine Learning September 23, 2026 By Xuanlin Jiang, Samuel Hsia, Michael Kuchnik, Zachary DeVito, Minlan Yu, Carole-Jean Wu

WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

3d ago

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v2 Announce Type: replace-cross Abstract: Modern LLM reinforcement learning (RL) workloads require a high-performance weight transfer system to scale training across heterogeneous com...

By Chenhao Ye, Huaizheng Zhang, Mingcong Han, Baoquan Zhong, Xiang Li, Qixiang Chen, Xinyi Zhang, Weidong Zhang, Kaihua Jiang, Wang Zhang, He Sun, Wencong Xiao, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau

llms reinforcement-learning

arXiv AI

Jun 16

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

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

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Sep 10

Miles v0.1: Production-Level Post-Training

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

agents diffusion reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jun 26

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

arXiv:2606. 26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models learn from ground-truth feedback on mathematical, logical, and scientific tasks.

By Rongjian Chen, Jianmin Hu, Kejiang Ye, Minxian Xu

llms reinforcement-learning benchmarks

arXiv AI

Sep 23

Conduit: An Experience Data Plane for Distributed Reinforcement Learning

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

reinforcement-learning

arXiv AI

Aug 25

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

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

llms agents reinforcement-learning

One email a morning, machine-written

Related stories

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

Miles v0.1: Production-Level Post-Training

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

Conduit: An Experience Data Plane for Distributed Reinforcement Learning

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning