AI RESEARCH
Learning CLI Agents with Structured Action Credit under Selective Observation
arXiv CS.AI
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ArXi:2605.08013v1 Announce Type: new Command line interface (CLI) agents are emerging as a practical paradigm for agent-computer interaction over evolving filesystems, executable command line programs, and online execution feedback. Recent work has used reinforcement learning (RL) to learn these interaction abilities from verifiable task feedback, yet few methods exploit the native structured attributes of CLI actions as learning signals. Beyond this underused action structure, CLI learning also couples two bottlenecks for coding agents.