arXiv:2605.24517v1 Announce Type: cross Abstract: CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned stream -- stdout, errors, files, logs, and traces -- records the consequences. We argue that this stream is a supervision signal, but standard agent RL discards it: GRPO-style training updates action tokens with sparse outcome-level rewards while ignoring environment responses already in the rollout. Failed rollouts provide little policy-gradient signal despite containing rich evidence about how th
This research builds on recent advancements in LLMs and agentic systems, addressing a core limitation in how these systems learn from environmental feedback, specifically within command-line interfaces.
Improving how AI agents learn from terminal interactions can significantly accelerate their development and capabilities, leading to more robust and autonomous systems across various digital tasks.
By enabling CLI agents to learn world models 'for free' from terminal output, this research offers a more efficient and data-rich method for agent training, potentially reducing the need for sparse human-engineered rewards.
- · AI agent developers
- · Companies using CLI-based automation
- · Software development industry
- · Traditional RL training methodologies
- · Manual software testing
AI agents become more efficient and capable at interacting with and learning from complex digital environments.
Accelerated development of more autonomous and intelligent software across engineering, operations, and cybersecurity.
Increased reliance on AI agents for crucial infrastructure management, posing new challenges for oversight and security.
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Read at arXiv cs.CL