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Reinforcement Learning Problem Solving with Large Language Models

arXiv 2404.18638 ↗First posted 29 Apr 2024cs.AI cs.CLcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) encapsulate an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of a variety of Natural Language Processing (NLP) tasks. This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems. However, one interesting avenue that shows untapped potential is the use of LLMs as Reinforcement Learning (RL) agents to enable conversational RL problem solving. Therefore, in this study, we explore the concept of formulating Markov Decision Process-based RL problems as LLM prompting tasks. We demonstrate how LLMs can be iteratively prompted to learn and optimize policies for specific RL tasks. In addition, we leverage the introduced prompting technique for episode simulation and Q-Learning, facilitated by LLMs. We then show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake" workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 29 Apr 2024.

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10 of 27 references.

References · 10

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  3. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  4. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 644
  5. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  6. 2022Can large language models reason about medical questions?cited by 194
  7. 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 152
  8. 2024ScreenAgent: A Vision Language Model-driven Computer Control Agentcited by 102
  9. 2023EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Educationcited by 80
  10. 2019Learning to Interactively Learn and Assistcited by 20

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  1. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
  2. 2025Feedback-Induced Performance Decline in LLM-Based Decision-Making
  3. 2025Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning
  4. 2023Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
  5. 2025A Survey of Reinforcement Learning for Large Reasoning Models
  6. 2025Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle
  7. 2024LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
  8. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models