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SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents

Research LLM Agents

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Merged summary

TL;DR - SkillForge is a reinforcement-learning framework that lets LLM agents accumulate reusable skills while continuously verifying and refining them through environment interaction. It improves over append-only skill banks by maintaining skill quality as agents learn across episodes.

  • Makes skill invocation explicit so RL jointly optimizes environment actions and decisions about when to use skills.
  • Uses interaction evidence to verify and refine stored skills rather than assuming they remain effective.
  • Supports multiple skill-induction pathways, enabling the skill bank to grow while controlling quality.
  • Consistently outperforms SkillRL in experiments on ALFWorld, WebShop, and AppWorld.

Sources (1)

SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents

arXiv cs.CL Shidong Yang, Ziyu Ma, Tongwen Huang, Xucong Wang, Renda Li, Yiming Hu, Yong Wang, Xiangxiang Chu 2026-08-25 arXiv:2608.24747
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-14 14:17:08.344088 UTC

TL;DR - SkillForge is a reinforcement-learning framework that lets LLM agents accumulate reusable skills while continuously verifying and refining them through environment interaction. It improves over append-only skill banks by maintaining skill quality as agents learn across episodes.

  • Makes skill invocation explicit so RL jointly optimizes environment actions and decisions about when to use skills.
  • Uses interaction evidence to verify and refine stored skills rather than assuming they remain effective.
  • Supports multiple skill-induction pathways, enabling the skill bank to grow while controlling quality.
  • Consistently outperforms SkillRL in experiments on ALFWorld, WebShop, and AppWorld.
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