arXiv AI

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.

arXiv AI

Jul 7

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.

By Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li, Kaiqiao Han, Chenyi Tong, Haoran Deng, Renliang Sun, Alexander Taylor, Yanqiao Zhu, Jason Cong, Yizhou Sun, Wei Wang

llms agents reinforcement-learning

arXiv Machine Learning

Jul 27

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.

By Shujin Wu, Cheng Qian, Xiusi Chen, Heng Ji

llms reinforcement-learning benchmarks

arXiv AI

Jul 3

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

arXiv:2607. 02440v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with open-ended software-engineering progress.

By Zhilin Wang, Han Song, Runzhe Zhan, Jusen Du, Jiacheng Chen, Tianle Li, Qingyu Yin, Yulun Wu, Zhennan Shen, Tong Zhu, Yanshu Li, Guanjie Chen, Derek F. Wong, Yafu Li, Yu Cheng, Yang Yang

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 3

EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents

arXiv:2606. 03678v1 Announce Type: new Abstract: Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures while preserving realism.

By Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Deng, Jian Sun, Wei Ma

llms agents robotics benchmarks safety

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

arXiv AI

Jun 9

MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution

arXiv:2606. 07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions.

By Bowen Ren, Heyan Huang, Yinghao Li, Yang Gao

llms agents benchmarks

arXiv AI

Jul 23

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.

By Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu

llms agents reinforcement-learning benchmarks

arXiv AI

1d ago

ClawGym II: Exploring Black-Box RL on Agent Harness

arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.

By Huatong Song, Fei Bai, Ming Yang, Renyuan Li, Jia Deng, Jujie He, Zhange Zhang, Daixuan Cheng, Yan Xing, Qi Yun, Xuxing Chen, Danyang Li, Feng Chang, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Mingjie Tang, Ji-Rong Wen

llms agents reinforcement-learning

arXiv Machine Learning

Jul 22

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents

arXiv:2508. 14751v2 Announce Type: replace Abstract: We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces.

By Thomas Carta, Cl\'ement Romac, Loris Gaven, Pierre-Yves Oudeyer, Olivier Sigaud, Sylvain Lamprier

llms agents reinforcement-learning

arXiv AI

1d ago

SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization

arXiv:2608. 14579v1 Announce Type: new Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures.

By Rui Yang

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 9

HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning

arXiv:2606. 08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms.

By Zechu Li, Yufeng Jin, Xiaoyang Liu, Puze Liu, Vignesh Prasad, Carlo D'Eramo, Georgia Chalvatzaki

agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 4

The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?

arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.

By Xinyu Lu, Tianshu Wang, Pengbo Wang, zujie wen, Zhiqiang Zhang, Jun Zhou, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

agents benchmarks safety

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