{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T10:16:56Z","timestamp":1781518616429,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1198","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10797-10806","source":"Crossref","is-referenced-by-count":1,"title":["Grounding Open-Domain Knowledge from LLMs to Real-World Reinforcement Learning Tasks: A Survey"],"prefix":"10.24963","author":[{"given":"Haiyan","family":"Yin","sequence":"first","affiliation":[{"name":"CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hangwei","family":"Qian","sequence":"additional","affiliation":[{"name":"CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxin","family":"Shi","sequence":"additional","affiliation":[{"name":"CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivor","family":"Tsang","sequence":"additional","affiliation":[{"name":"CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore"},{"name":"College of Computing and Data Science, Nanyang Technological University (NTU), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yew-Soon","family":"Ong","sequence":"additional","affiliation":[{"name":"CFAR and IHPC, Agency for Science, Technology and Research (A*STAR), Singapore"},{"name":"College of Computing and Data Science, Nanyang Technological University (NTU), Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:25Z","timestamp":1758627385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1198"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1198","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}