@inproceedings{lippmann-etal-2025-positive,
    title = "Positive Experience Reflection for Agents in Interactive Text Environments",
    author = "Lippmann, Philip  and
      Spaan, Matthijs T. J.  and
      Yang, Jie",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.realm-1.10/",
    pages = "131--142",
    ISBN = "979-8-89176-264-0",
    abstract = "Intelligent agents designed for interactive environments face significant challenges in text-based games, a domain that demands complex reasoning and adaptability. While agents based on large language models (LLMs) using self-reflection have shown promise, they struggle when initially successful and exhibit reduced effectiveness when using smaller LLMs. We introduce Sweet{\&}Sour, a novel approach that addresses these limitations in existing reflection methods by incorporating positive experiences and managed memory to enrich the context available to the agent at decision time. Our comprehensive analysis spans both closed- and open-source LLMs and demonstrates the effectiveness of Sweet{\&}Sour in improving agent performance, particularly in scenarios where previous approaches fall short."
}