arXiv AI June 2, 2026 By Danlong Yuan, Wei Wu, Enhan Zhao, Zhengren Wang, Xueliang Zhao, Huishuai Zhang, Dongyan Zhao

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents

arXiv:2602. 11210v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation.

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 Machine Learning

Jul 3

The Rollout Infrastructure Tax in Coding-Agent Reinforcement Learning

arXiv:2607. 01415v1 Announce Type: new Abstract: Coding-agent reinforcement learning treats execution infrastructure as a background implementation detail, despite relying on large numbers of interactive software rollouts.

By Daniel Thi Graviet, Lovre Pesut, Ivan Dagelic, Vedran Jukic, Ivan Burazin

agents reinforcement-learning

arXiv AI

Jun 30

Dockerless: Environment-Free Program Verifier for Coding Agents

arXiv:2606. 28436v1 Announce Type: cross Abstract: Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL).

By Wenhao Zeng, Yuling Shi, Xiaodong Gu, Chao Hu, Chaofan Wang, Yuhao Cui, Hongting Zhou, Mengnan Qi, Jianqiao Wangni, Zhaojian Yu, Shuzheng Gao, Kai Cai, Shilin He

agents reinforcement-learning fine-tuning benchmarks

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 14

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

arXiv:2607. 11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution.

By Bowen Lv, Xiao Liu, Yanyu Ren, Hanyu Lai, Bohao Jing, Hanchen Zhang, Yanxiao Zhao, Shuntian Yao, Jie Tang, Yuxiao Dong

agents computer-vision reinforcement-learning benchmarks

arXiv AI

Jun 19

ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis

arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.

By Guohong Liu, Jialei Ye, Pengzhi Gao, Wei Liu, Jian Luan, Yunxin Liu, Yuanchun Li

llms agents benchmarks

arXiv AI

Jul 14

ML in a Box: Analyzing Containerization Practices in Open Source ML Projects

arXiv:2607. 10126v1 Announce Type: cross Abstract: Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency.

By Faten Jebari, Emna Ksontini, Amine Barrak, Wael Kessentini

One email a morning, machine-written

Related stories

The Rollout Infrastructure Tax in Coding-Agent Reinforcement Learning

Dockerless: Environment-Free Program Verifier for Coding Agents

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

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

ScaleWoB: Guiding GUI Agents with Coding Agents via Large-Scale Environmental Synthesis

ML in a Box: Analyzing Containerization Practices in Open Source ML Projects