@inproceedings{wang-etal-2026-reinforcement,
    title = "Reinforcement Learning for Self-Improving Agent with Skill Library",
    author = "Wang, Jiongxiao  and
      Yan, Qiaojing  and
      Wang, Yawei  and
      Tian, Yijun  and
      Mishra, Soumya Smruti  and
      Xu, Zhichao  and
      Gandhi, Megha  and
      Xu, Panpan  and
      Cheong, Lin Lee",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.69/",
    pages = "1529--1550",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely primarily on LLM prompting, making consistent skill library implementation challenging. To overcome these challenges, we propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library. Specifically, we introduce Skill Augmented GRPO for self-Evolution (SAGE), a novel RL framework that systematically incorporates skills into learning. The framework{'}s key component, Sequential Rollout, iteratively deploys agents across a chain of similar tasks for each rollout. As agents navigate through the task chain, skills generated from previous tasks accumulate in the library and become available for subsequent tasks. Additionally, the framework enhances skill generation and utilization through a Skill-integrated Reward that complements the original outcome-based rewards. Experimental results on AppWorld demonstrate that SAGE, when applied to supervised-finetuned model with expert experience, achieves 8.9{\%} higher Scenario Goal Completion while requiring 26{\%} fewer interaction steps and generating 59{\%} fewer tokens, substantially outperforming existing approaches in both accuracy and efficiency. Our code is available at https://github.com/amazon-science/SAGE."
}