arXiv AI June 2, 2026 By Zelin He, Haotian Lin, Boran Han, Wei Zhu, Haoyang Fang, Bernie Wang, Xuan Zhu, Runze Li, Matthew Reimherr

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL

Read the original on arXiv AI →

arXiv:2606. 01619v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks.

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arXiv AI

2d ago

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

arXiv:2605.08693v3 Announce Type: replace Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and...

By Min Yang, Jinghua Piao, Xu Xia, Xiaochong Lan, Jiaju Chen, Yongshun Gong, Yong Li

llms agents benchmarks

arXiv AI

5d ago

CODESKILL: Learning Self-Evolving Skills for Coding Agents

CODESKILL is an LLM-based framework that learns to extract, evolve, and maintain procedural skills from coding-agent trajectories. It treats skill extraction and skill-bank management as a learnable policy trained with reinforcement learning, using a hybrid reward combining rubric-based skill quality and verifiable execution feedback. Experiments on EnvBench, SWE-Bench Verified, and Terminal-Bench 2 demonstrate that CODESKILL raises average pass rates by 11.03 over a no-skill baseline and by 5.10 over the strongest prompt-based or memory baseline while keeping a compact skill bank.

By Yanzhou Li, Yiran Zhang, Xiaoyu Zhang, Xiaoxia Liu, Yang Liu

llms agents reinforcement-learning

arXiv Computation and Language

Aug 21

SPyCE: Skill-Policy Co-evolution for Multimodal Agents

arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.

By Ru Zhang, Weijie Qiu

agents reinforcement-learning multimodal benchmarks

arXiv AI

Jul 24

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

arXiv:2607. 21419v1 Announce Type: new Abstract: In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization.

By Yipeng Shi, Zhipeng Ma, Yue Wang, Qitai Tan, Yang Li, Peng Chen, Zhengzhou Zhu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 30

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.

By Zhiyuan Yao, Yuxin Chen, Zhengxi Lu, Zishan Xu, Yueqing Sun, Yifu Guo, Yuquan Lu, Zhengzhou Cai, Kangning Zhang, Zhuowen Han, Zi-Han Wang, Ziang Ye, Qi Gu, Xunliang Cai, Weiwen Liu, Yongliang Shen

llms agents reinforcement-learning

arXiv AI

Jun 3

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

arXiv:2606. 03108v1 Announce Type: new Abstract: Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static.

By Guhong Chen, Yingcheng Shi, Yongbin Li, Binhua Li, Xander Xu, Hu Wei, Shiwen Ni, Min Yang, Jieping Ye

llms agents reinforcement-learning

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Related stories

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

CODESKILL: Learning Self-Evolving Skills for Coding Agents

SPyCE: Skill-Policy Co-evolution for Multimodal Agents

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning