Yin, H., Qian, H., Shi, Y., Tsang, I., & Ong, Y. (2025). Grounding Open-Domain Knowledge from LLMs to Real-World Reinforcement Learning Tasks: A Survey. Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence Survey Track, 10797–10806. https://doi.org/10.24963/ijcai.2025/1198
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 realworld 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 finetuning paradigms that adapt LLMs to environmentspecific 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 nextgeneration RL systems that integrate open-domain knowledge with adaptive learning.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Fund (CDF)
Grant Reference no. : C233312007
This research / project is supported by the National Research Foundation, Singapore - AI Singapore Programme
Grant Reference no. : AISG-NMLP2024-003