Learning CLI Agents with Structured Action Credit under Selective Observation
📰 ArXiv cs.AI
Learn how to train CLI agents using structured action credit under selective observation to improve interaction abilities
Action Steps
- Implement a reinforcement learning framework to train CLI agents
- Use structured action credit as a learning signal to exploit native attributes of CLI actions
- Apply selective observation to filter relevant feedback and improve learning efficiency
- Configure the agent to interact with evolving filesystems and executable command line programs
- Test the agent's performance on verifiable task feedback
Who Needs to Know This
Researchers and developers working on CLI agents and reinforcement learning can benefit from this knowledge to improve their models
Key Insight
💡 Structured action credit can be used as a learning signal to improve CLI agent performance
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🤖 Train CLI agents with structured action credit under selective observation to boost interaction abilities! #CLIagents #RL
Key Takeaways
Learn how to train CLI agents using structured action credit under selective observation to improve interaction abilities
Full Article
Title: Learning CLI Agents with Structured Action Credit under Selective Observation
Abstract:
arXiv:2605.08013v1 Announce Type: new Abstract: 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 le
Abstract:
arXiv:2605.08013v1 Announce Type: new Abstract: 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 le
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