SkillRise: A Unified RL Framework for Cross-Task Skill Evolution in LLM Agents
Zhiyuan Yao · hf · 2026-07-30
SkillRise is a unified reinforcement learning framework designed for LLM agents to learn and evolve skills across various tasks.
- The Problem: Standard agentic RL treats tasks as independent episodes, while existing skill learning methods either focus on single tasks or rely on complex multi-stage pipelines.
- The Method: SkillRise organizes related tasks into progressively challenging sequences. A single policy alternates between task solving and curating an evolving skill document passed directly to the next task. It uses decoupled credit assignment: supervising solving with current task outcomes and curation with discounted downstream outcomes.
- Results: Achieves state-of-the-art Pass@1 performance on ALFWorld, WebShop, and ScienceWorld, outperforming the strongest baselines by 2.3 to 8.5 percentage points. It also demonstrates test-time scaling across tasks and significantly reduces runtime overhead compared to multi-stage pipelines.
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