Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

Analysis

Why This Paper Matters

Large language models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, leveraging extensive pretrained knowledge. Reinforcement learning (RL), while powerful, often struggles with sample efficiency, multitask learning, and high-level planning. This survey bridges these two fields by systematically reviewing how LLMs can enhance RL. Given the rapid growth of research at the intersection of LLMs and RL, a structured taxonomy is timely and essential for clarifying the landscape and guiding future work.

The paper's significance lies in its comprehensive categorization of LLM roles in RL, which helps researchers navigate the burgeoning literature. By identifying four distinct roles—information processor, reward designer, decision-maker, and generator—the authors provide a clear framework that highlights how LLMs can address specific RL challenges. This taxonomy not only organizes existing methods but also reveals gaps and opportunities for future research.

Moreover, the survey emphasizes practical applications in complex domains such as robotics, autonomous driving, and energy systems, where RL is increasingly deployed. By leveraging LLMs' high-level reasoning and generalization, these applications could become more robust and efficient. Thus, this paper serves as a foundational reference for both RL and LLM communities, fostering cross-disciplinary innovation.

Technical Contributions

• Taxonomy of LLM Roles in RL: The paper introduces a structured taxonomy based on the agent-environment interaction paradigm, categorizing LLMs as information processors (e.g., state representation, feature extraction), reward designers (e.g., reward shaping, preference modeling), decision-makers (e.g., policy generation, action selection), and generators (e.g., environment generation, data augmentation).
• Methodological Review: For each role, the authors summarize representative methodologies, providing a comprehensive overview of techniques such as prompt engineering, fine-tuning, and in-context learning applied to RL tasks.
• Challenge Analysis: The survey analyzes specific RL challenges mitigated by each role, such as improving sample efficiency through LLM-generated rewards or enabling multitask learning via LLM-based task decomposition.
• Future Directions: Insights into prospective research directions are provided, including the need for better evaluation benchmarks, safety considerations, and integration with other modalities.

Results

As a survey, this paper does not present empirical results. Instead, it offers a qualitative comparative analysis of the four roles, discussing their strengths, weaknesses, and applicability. The authors synthesize findings from existing literature to illustrate how LLMs can enhance RL in aspects like multitask learning, sample efficiency, and high-level task planning. The paper also identifies potential applications and challenges, such as computational overhead, interpretability, and safety, providing a balanced view of the field's current state.

Significance

The proposed taxonomy provides a common language for researchers to discuss and compare LLM-enhanced RL methods, facilitating collaboration and accelerating progress. By clarifying the roles LLMs can play, the survey helps practitioners select appropriate techniques for their specific RL problems. Furthermore, the discussion of applications in robotics, autonomous driving, and energy systems highlights the practical relevance of this research. As LLMs continue to evolve, their integration with RL is expected to yield more capable and generalizable agents, and this survey lays the groundwork for such advancements by organizing the field and pointing toward future research directions.