Hugging Face Trending Papers June 3, 2026

AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning

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We present AgentJet, a distributed swarm training framework for large language model (LLM) agent reinforcement learning. Unlike centralized frameworks that tightly couple agent rollouts with model optimization, AgentJet adopts a decoupled multi-node architecture in which swarm server nodes host trainable models and run optimization on GPU clusters, whereas swarm client nodes execute arbitrary agents on arbitrary devices.

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arXiv AI

Jun 4

AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning

arXiv:2606. 04484v1 Announce Type: new Abstract: We present AgentJet, a distributed swarm training framework for large language model (LLM) agent reinforcement learning.

By Qingxu Fu, Boyin Liu, Shuchang Tao, Zhaoyang Liu, Bolin Ding

llms agents reinforcement-learning

arXiv AI

Jul 22

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning

arXiv:2606. 04484v2 Announce Type: replace Abstract: Training reinforcement learning (RL) policies for large language model (LLM) agents requires optimizing multi-turn trajectories that interact with external environments.

By Qingxu Fu, Boyin Liu, Shuchang Tao, Zhaoyang Liu, Cheng Chen, Xuanfa Jin, Rong Zhu, Bolin Ding

llms agents 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

arXiv AI

Jul 9

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

arXiv:2607. 07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs).

By Zhenyu Hou, Yujiang Li, Jie Tang, Yuxiao Dong

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Jun 3

EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management

arXiv:2606. 03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science.

By Zherui Yang, Fan Liu, Yansong Ning, Hao Liu

llms agents reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 8

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks.

llms agents reinforcement-learning efficiency benchmarks

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