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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.
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).
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.
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.
arXiv:2607. 10126v1 Announce Type: cross Abstract: Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency.
arXiv:2602. 09345v3 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations.