Trackable Skill Library Lifecycle Management for Agentic Reinforcement Learning

Key points

• Skill-augmented agentic RL has emerged as an effective approach to improving LLM agents on long-horizon tasks.1
• However, existing skill library systems fail to identify each skill's true contribution due to free-riding among co-retrieved skills, difficulty confounding across tasks, and the absence of skill-specific counterfactual evidence, which prevents reliable skill lifecycle management.1
• Along this line, we formulate skill lifecycle management as an evidence-gated state-transition process that governs each skill from generation and admission through protected incubation to retention, retirement, or removal, while a soft capacity bound controls library growth.1
• Experiments show that TrackRL outperforms recent skill-augmented RL baselines in both library-assisted and skill-free evaluation, with further analyses revealing how skill value shifts during policy learning and how evidence-driven state transitions shape skill library evolution.1

Sources (1)

• [1]Trackable Skill Library Lifecycle Management for Agentic Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 09:15 AM

  We propose TrackRL, an agentic RL framework where skill tracking drives skill library full lifecycle management.

  Skill-augmented agentic RL has emerged as an effective approach to improving LLM agents on long-horizon tasks.

Extractive summary: sentences quoted from the sources.