The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning
It consolidates existing work for researchers in RL and AI, but is incremental as a survey without new results.
This survey reviews research integrating Large Language Models (LLMs) and Vision-Language Models (VLMs) into reinforcement learning (RL) to address challenges like lack of prior knowledge and long-horizon planning, establishing a taxonomy and identifying open problems to advance unified approaches.
Reinforcement learning (RL) has shown impressive results in sequential decision-making tasks. Meanwhile, Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged, exhibiting impressive capabilities in multimodal understanding and reasoning. These advances have led to a surge of research integrating LLMs and VLMs into RL. In this survey, we review representative works in which LLMs and VLMs are used to overcome key challenges in RL, such as lack of prior knowledge, long-horizon planning, and reward design. We present a taxonomy that categorizes these LLM/VLM-assisted RL approaches into three roles: agent, planner, and reward. We conclude by exploring open problems, including grounding, bias mitigation, improved representations, and action advice. By consolidating existing research and identifying future directions, this survey establishes a framework for integrating LLMs and VLMs into RL, advancing approaches that unify natural language and visual understanding with sequential decision-making.