SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
arXiv:2602.11210v4 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. At scale, pre-built container images incur substantial storage overhead, slow environment setup, and require container-management privileges. We propose SWE-MiniSandbox, a lightweight, container-free method that enables scalable RL training of SWE agents without sacrificing isolation. Instead of relying on per-instance containers, SWE-MiniSandbox executes each t
The increasing computational demands and storage overheads of training sophisticated AI agents necessitate more efficient and scalable infrastructure solutions.
This development addresses a critical bottleneck in the scalable deployment and training of AI agents, potentially accelerating their development and adoption across industries.
The reliance on heavy, per-task container images for AI agent training is being superseded by lightweight, container-free approaches, enabling more agile and widespread experimentation.
- · AI Agent developers
- · Cloud providers with optimized infrastructure
- · Software engineering automation
- · Containerization-focused AI infrastructure providers
- · Companies with legacy AI agent training pipelines
Faster and cheaper development cycles for sophisticated AI agents become possible.
The proliferation of AI agents leads to more widespread automation in white-collar tasks.
Increased accessibility to agent training infrastructure democratizes advanced AI development, fostering innovation across smaller teams and startups.
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Read at arXiv cs.LG