UCOB: Learning to Utilize and Evolve Agentic Skills via Credit-Aware On-Policy Bidirectional Self-Distillation
arXiv:2606.29502v1 Announce Type: new Abstract: Skill memories can improve agentic reinforcement learning by reusing past experience as textual guidance, but retrieved skills are not oracular: they may help in one state while misleading the same policy in another. This makes the common privileged-teacher assumption fragile, namely that a skill-conditioned prompt can be treated as a fixed teacher for the no-skill prompt. We introduce UCOB, a framework for learning to utilize and evolve agentic skills via credit-aware on-policy bidirectional self-distillation. UCOB treats skill-conditioned and n
The continuous evolution of AI capabilities necessitates more robust and adaptive learning frameworks for agentic systems, moving beyond static skill assumptions.
This framework addresses a core limitation in agentic reinforcement learning, enabling more dynamic and reliable skill utilization in complex, uncertain environments.
AI agents can now learn to both leverage and refine their acquired skills in a bidirectional manner, making their guidance more adaptive rather than fixed.
- · AI agents developers
- · Robotics
- · Automation companies
- · Fixed-skill AI systems
- · Developers relying on static agentic skill models
Improved performance and adaptability of AI agents in real-world scenarios.
Accelerated development and deployment of sophisticated autonomous systems across various industries.
Increased reliability and trustworthiness of AI agents leading to broader societal integration.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI