{"type":"ScholarlyArticle","title":"Grounding Open-Domain Knowledge from LLMs to Real-World Reinforcement Learning Tasks: A Survey","authors":[{"name":"Haiyan Yin","slug":"haiyan-yin","given":"Haiyan","family":"Yin","affiliation":""},{"name":"Hangwei Qian","slug":"hangwei-qian","given":"Hangwei","family":"Qian","affiliation":""},{"name":"Yaxin Shi","slug":"yaxin-shi","given":"Yaxin","family":"Shi","affiliation":""},{"name":"Ivor Tsang","slug":"ivor-tsang","given":"Ivor","family":"Tsang","affiliation":""},{"name":"Yew-Soon Ong","slug":"yew-soon-ong","given":"Yew-Soon","family":"Ong","affiliation":""}],"abstract":"Grounding open-domain knowledge from large language models (LLMs) into real-world reinforcement learning (RL) tasks represents a transformative frontier in developing intelligent agents capable of advanced reasoning, adaptive planning, and robust decision-making in dynamic environments. In this paper, we introduce the LLM-RL Grounding Taxonomy, a systematic framework that categorizes emerging methods for integrating LLMs into RL systems by bridging their open-domain knowledge and reasoning capabilities with the task-specific dynamics, constraints, and objectives inherent to real-world RL environments. This taxonomy encompasses both training-free approaches, which leverage the zero-shot and few-shot generalization capabilities of LLMs without fine-tuning, and fine-tuning paradigms that adapt LLMs to environment-specific tasks for improved performance. We critically analyze these methodologies, highlight practical examples of effective knowledge grounding, and examine the challenges of alignment, generalization, and real-world deployment. Our work not only illustrates the potential of LLM-RL agents for enhanced decision-making, but also offers actionable insights for advancing the design of next-generation RL systems that integrate open-domain knowledge with adaptive learning.","doi":"10.24963/ijcai.2025/1198","url":"https://ijcai.org/proceedings/2025/1198","pdf":"https://ijcai.org/proceedings/2025/1198.pdf","markdown":"https://ijcai.org/proceedings/2025/1198.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1198","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1198,"pages":{"first":10797,"last":10806},"keywords":["Natural Language Processing: NLP: Applications","Machine Learning: ML: Reinforcement learning","Natural Language Processing: NLP: Language models"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}