arXiv:2605.09192v2 Announce Type: replace Abstract: Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without environment-grounded verification. Existing skill generation methods heavily rely on preference logs rather than direct environment interaction, often yielding negligible or even degraded gains. We identify that it is a fundamental timing bottleneck: robust skills should be posterior-based, distilled from empirical environment interaction rather than prior plans. In this study, we introduce the P
The proliferation of AI agents operating in complex environments necessitates more robust and verifiable skill development methods to prevent degraded performance.
This research addresses a fundamental bottleneck in AI agent development, shifting from hypothetical plans to real-world verified interactions, which is critical for trustworthy autonomous systems.
The focus for AI skill distillation moves from preference-based methods to empirically verifiable, environment-grounded feedback, potentially leading to more reliable and effective agent behaviors.
- · AI Agent developers
- · Robotics
- · Autonomous systems
- · AI methods relying solely on 'plan-based' skill generation
- · Unverified AI agent applications
AI agents become significantly more reliable and capable in complex, dynamic environments.
Accelerated deployment and adoption of AI agents across various industries due to increased trust and performance.
This enhanced reliability could enable more complex autonomous systems to interact directly with critical infrastructure or human safety scenarios.
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Read at arXiv cs.AI