Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
Summary
Skill1 is a unified framework that trains a single policy to co-evolve skill selection, utilization, and distillation using a shared task-outcome objective. Experiments on ALFWorld and WebShop show it outperforms existing baselines in complex task environments.
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Paper page - Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
Source: https://huggingface.co/papers/2605.06130
Abstract
Skill1 is a unified framework that trains a single policy to simultaneously evolve skill selection, utilization, and distillation capabilities using a shared task-outcome objective, demonstrating superior performance over existing baselines in complex task environments.
A persistentskill libraryallows language model agents to reuse successful strategies across tasks. Maintaining such a library requires three coupled capabilities. The agent selects a relevant skill, utilizes it during execution, and distills new skills from experience. Existing methods optimize these capabilities in isolation or with separate reward sources, resulting in partial and conflicting evolution. We propose Skill1, a framework that trains a single policy to co-evolveskill selection, utilization, and distillation toward a sharedtask-outcome objective. The policy generates a query to search theskill library, re-ranks candidates to select one, solves the task conditioned on it, and distills a new skill from the trajectory. All learning derives from a single task-outcome signal. Its low-frequency trend credits selection and its high-frequency variation credits distillation. Experiments onALFWorldandWebShopshow that Skill1 outperforms prior skill-based andreinforcement learningbaselines. Training dynamics confirm the co-evolution of the three capabilities, and ablations show that removing any credit signal degrades the evolution.
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