From the 1 of 10 linked papers with an AI index.
10 papers
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Zishan Xu, Zhiyuan Yao, Yuxin Chen +9
Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verificatio…
Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
Shuai Shao, Kangning Zhang, Qingyao Li +7
Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weigh…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13
SkillRise introduces a reinforcement‑learning framework that lets large language model agents learn and reuse transferable skills across related tasks by curating a skill document…
ICRL: Learning to Internalize Self-Critique with Reinforcement Learning
Jianbo Lin, Xiaomin Yu, Yi Xin +7
Large language model-based agents make mistakes, yet critique can often guide the same model toward correct behavior. However, when critique is removed, the model may fail again on…
Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
Zhiqing Cui, Haotong Xie, Jiahao Yuan +11
Large language model (LLM) multi-agent systems typically rely on rigid orchestration, committing either to flat per-query routing or to hand-engineered task decomposition, so decom…
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
Beining Wu, Fuyou Mao, Jiong Lin +7
Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimiz…