arXiv AI June 2, 2026 By Hongyu Lin, Yuchen Li, Haoran Luo, Zhenghong Lin, Libo Zhang, Mingjie Xing, Yanjun Wu

TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

arXiv:2508. 12551v2 Announce Type: replace-cross Abstract: Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sparse performance feedback, and strong workload sensitivity.

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 AI.

arXiv AI

Jun 26

EGG: An Expert-Guided Agent Framework for Kernel Generation

arXiv:2606. 26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts.

By Yaochen Han, Ke Fan, Hongxu Jiang, Wanqi Xu, Weiyu Xie, Runhua Zhang, Chenhui Zhu, Yixiang Zhang

llms agents reinforcement-learning

arXiv AI

Jun 24

Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation

arXiv:2606. 24515v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces.

By Marta Sumyk, Oleksandr Kosovan

llms agents reinforcement-learning fine-tuning multimodal

arXiv AI

Jul 24

From Agent Failures to Text Policies: What Works and What Breaks

arXiv:2607. 20668v1 Announce Type: cross Abstract: TextGrad improves language-model systems by revising text from feedback.

By Jaideep Ray, Ankit Goyal

llms agents

arXiv AI

Jul 14

SETA: Scaling Environments for Terminal Agents

arXiv:2607. 10891v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs).

By Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru, Aznaur Aliev, Jay Rainton, Ahmed Awelkair, Zhichen Zeng, Jiajun Li, Shi Dong, Yueming Yuan, Boyuan Ma, Qizheng Zhang, Jiwei Fu, Yuzhen Mao, Wendong Fan, Ping Nie, Philip Torr, Bernard Ghanem, Changran Hu, Jonathan Lingjie Li, Urmish Thakker, Guohao Li

llms agents reinforcement-learning

arXiv AI

Sep 10

EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent

arXiv:2609.05576v1 Announce Type: new Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across statefu...

By Yirong Zeng, Shen You, Jinhang Feng, Yufei Liu, Xiao Ding, Yutai Hou, Hao Cong, Yuxian Wang, Wu Ning, Wang Xu, Bibo Cai

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 8

Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning

arXiv:2607. 05773v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making.

By Akshay Arora, Ishan Nigam, Ashutosh Aggarwal, Shefali Bansal, Krishna Singh, Sweta Kumari, Nikhil Mittal, Shariq Farhan, Siddarth Malreddy

llms agents reinforcement-learning

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Related stories

EGG: An Expert-Guided Agent Framework for Kernel Generation

Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation

From Agent Failures to Text Policies: What Works and What Breaks

SETA: Scaling Environments for Terminal Agents

EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent

Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning