CLI Agents Learn from Structured Action Credit and Selective Observation
A recent paper on arXiv (2605.08013) presents strategies to enhance command line interface (CLI) agents by utilizing structured action attributes and selective observation. The researchers highlight two primary challenges: extracting task-relevant information from partial observations within extensive codebases and providing sparse terminal rewards for actions across lengthy multi-turn sequences. They propose σ-Reveal, a mechanism for inference time that curates token-budgeted context for the same CLI. The study examines these methods through tasks involving shell-driven information extraction and file editing. Employing reinforcement learning (RL), the research aims to develop interaction skills based on verifiable task feedback, taking advantage of the inherent structured attributes of CLI actions as learning signals.
Key facts
- Paper arXiv:2605.08013 proposes CLI agent improvements.
- Introduces σ-Reveal for selective observation.
- Addresses bottlenecks in large codebase navigation and reward assignment.
- Uses reinforcement learning from verifiable task feedback.
- Focuses on shell-driven information extraction and file editing tasks.
- Exploits structured attributes of CLI actions as learning signals.
- CLI agents interact with evolving filesystems and programs.
- Work is an inference-time mechanism for token-budgeted context selection.
Entities
Institutions
- arXiv