Skill-R1: Agent Skill Evolution via Reinforcement Learning
Researchers have introduced Skill-R1, a novel reinforcement learning framework designed to optimize skills for agentic large language models (LLMs). Unlike traditional methods that rely on costly prompt engineering or direct model alignment, Skill-R1 trains a lightweight skill generator to steer a frozen task LLM. This approach ensures compatibility with both open- and closed-source black-box models while significantly reducing adaptation costs. The framework operates through a recurrent process where verified outcomes from current rollouts inform subsequent skill revisions. To optimize this evolution, the authors propose a bi-level group-relative policy optimization objective, combining intra-generation and inter-generation advantages to ensure directional improvement rather than one-shot refinement. Empirical results demonstrate that Skill-R1 achieves consistent performance gains over no-skill baselines and standard GRPO methods across various benchmarks. The framework shows particularly strong improvements in complex, multi-step tasks, offering a principled solution for instance-level skill optimization using verifiable rewards.
Wire timeline
Skill-R1: Agent Skill Evolution via Reinforcement Learning
Researchers have introduced Skill-R1, a novel reinforcement learning framework designed to optimize skills for agentic large language models (LLMs). Unlike traditional methods that rely on costly prompt engineering or direct model alignment, Skill-R1 trains a lightweight skill generator to steer a frozen task LLM. This approach ensures compatibility with both open- and closed-source black-box models while significantly reducing adaptation costs. The framework operates through a recurrent process where verified outcomes from current rollouts inform subsequent skill revisions. To optimize this evolution, the authors propose a bi-level group-relative policy optimization objective, combining intra-generation and inter-generation advantages to ensure directional improvement rather than one-shot refinement. Empirical results demonstrate that Skill-R1 achieves consistent performance gains over no-skill baselines and standard GRPO methods across various benchmarks. The framework shows particularly strong improvements in complex, multi-step tasks, offering a principled solution for instance-level skill optimization using verifiable rewards.
cs.AI updates on arXiv.org