@inproceedings{k-etal-2025-study,
    title = "A Study on Leveraging Search and Self-Feedback for Agent Reasoning",
    author = "K, Karthikeyan  and
      Yuan, Michelle  and
      Mansimov, Elman  and
      Margatina, Katerina  and
      Pratik, Anurag  and
      Bonadiman, Daniele  and
      Sunkara, Monica  and
      Zhang, Yi  and
      Benajiba, Yassine",
    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/landing_page/2025.realm-1.18/",
    pages = "259--271",
    ISBN = "979-8-89176-264-0",
    abstract = "Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the ground truth or rely on model{'}s own generated feedback. The search algorithm uses this feedback to then produce values that will update its criterion for exploring and exploiting various reasoning paths. In this study, we investigate how search and model{'}s self-feedback can be leveraged for reasoning tasks. First, we explore differences in ground-truth feedback and self-feedback during search for math reasoning. Second, we observe limitations in applying search techniques to more complex tasks like tool-calling and design domain-specific approaches to address these gaps. Our experiments reveal challenges related to generalization when solely relying on self-feedback during search. For search to work effectively, either access to the ground-truth is needed or feedback mechanisms need to be carefully designed for the specific task."
}