mlsanityData to 7 Oct 2026

Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

arXiv 2306.03604 ↗First posted 6 Jun 2023cs.AIcited by 28

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions. However, interactions with LLMs can be time-consuming. In many practical scenarios, it requires a significant amount of storage space that can only be deployed on remote cloud servers. Additionally, using commercial LLMs can be costly since they may charge based on usage frequency. In this paper, we explore how to enable intelligent cost-effective interactions between a down stream task oriented agent and an LLM. We find that this problem can be naturally formulated by a Markov decision process (MDP), and propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions to accomplish a target task. On one side, When2Ask discourages unnecessary redundant interactions, while on the other side, it enables the agent to identify and follow useful instructions from the LLM. This enables the agent to halt an ongoing plan and transition to a more suitable one based on new environmental observations. Experiments on MiniGrid and Habitat environments that entail planning sub-goals demonstrate that When2Ask learns to solve target tasks with only a few necessary interactions with the LLM, significantly reducing interaction costs in testing environments compared with baseline methods. Our code is available at: https://github.com/ZJLAB-AMMI/LLM4RL.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 19 of its 31 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

19 of 31 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Models25 May

Other references · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2023A Survey of Large Language Modelscited by 2,537
  7. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  8. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  9. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,057
  10. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  11. 2022VIMA: General Robot Manipulation with Multimodal Promptscited by 403
  12. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 396
6 more
  1. 2017Embodied Question Answeringcited by 335
  2. 2018BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learningcited by 238
  3. 2023Grounding Large Language Models in Interactive Environments with Online Reinforcement Learningcited by 193
  4. 2023Collaborating with language models for embodied reasoningcited by 61
  5. 2022Retrospectives on the Embodied AI Workshopcited by 46
  6. 2023Towards Commonsense Knowledge based Fuzzy Systems for Supporting Size-Related Fine-Grained Object Detectioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 9; 2025: 11; 2026: 2.

28 papers.

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  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  4. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospectscited by 130
  5. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  6. 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 96
  7. 2023Large Language Model as a Policy Teacher for Training Reinforcement Learning Agentscited by 30
  8. 2024LLM-Empowered State Representation for Reinforcement Learningcited by 28
  9. 2024Multi-Agent Large Language Models for Conversational Task-Solvingcited by 24
  10. 2024Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity gamescited by 20
  11. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  12. 2025Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agentscited by 18
16 more
  1. 2025The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learningcited by 10
  2. 2024A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedbackcited by 9
  3. 2025Reasoning through Exploration: A Reinforcement Learning Framework for Robust Function Callingcited by 7
  4. 2023Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptationcited by 6
  5. 2024Multi-granularity Knowledge Transfer for Continual Reinforcement Learningcited by 6
  6. 2024Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Modelscited by 4
  7. 2025Agentic AI for autonomous anomaly management in complex systemscited by 4
  8. 2025Sample Efficient Reinforcement Learning via Large Vision Language Model Distillationcited by 3
  9. 2024SEAL: SEmantic-Augmented Imitation Learning via Language Modelcited by 1
  10. 2023ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operationscited by 0
  11. 2025Application of LLMs to Multi-Robot Path Planning and Task Allocationcited by 0
  12. 2025Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systemscited by 0
  13. 2025Goal-Guided Efficient Exploration via Large Language Model in Reinforcement Learningcited by 0
  14. 2025Instruction-Tuning Open-Weight Language Models for BPMN Model Generationcited by 0
  15. 2026Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphscited by 0
  16. 2026No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agentscited by 0

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