Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks

2025/12/03 by Cai, Lingyi, Fu, Wenjie, Huang, Yuxi +9
#FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI)

paper · doi:10.48550/arxiv.2512.03722

Abstract

Reinforcement Learning (RL) has shown remarkable success in enabling adaptive and data-driven optimization for various applications in wireless networks. However, classical RL suffers from limitations in generalization, learning feedback, interpretability, and sample efficiency in dynamic wireless environments. Large Language Models (LLMs) have emerged as a transformative Artificial Intelligence (AI) paradigm with exceptional capabilities in knowledge generalization, contextual reasoning, and interactive generation, which have demonstrated strong potential to enhance classical RL. This paper serves as a comprehensive tutorial on LLM-enhanced RL for wireless networks. We propose a taxonomy to categorize the roles of LLMs into four critical functions: state perceiver, reward designer, decision-maker, and generator. Then, we review existing studies exploring how each role of LLMs enhances different stages of the RL pipeline. Moreover, we provide a series of case studies to illustrate how to design and apply LLM-enhanced RL in low-altitude economy networking, vehicular networks, and space-air-ground integrated networks. Finally, we conclude with a discussion on potential future directions for LLM-enhanced RL and offer insights into its future development in wireless networks.

Citations

• LLM Agent Communication Protocol (LACP) Requires Urgent Standardization: A Telecom-Inspired Protocol is Necessary
• Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution
• Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
• ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
• Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing
• Chain-of-Thought for Large Language Model-empowered Wireless Communications
• Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking
• The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks
• Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models
• LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks
• Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey
• Adopting Large Language Models to Automated System Integration
• Artificial Intelligence Index Report 2025
• Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments
• ORANSight-2.0: Foundational LLMs for O-RAN
• An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning
• LLMKey: LLM-Powered Wireless Key Generation Scheme for Next-Gen IoV Systems
• A Survey on DRL based UAV Communications and Networking: DRL Fundamentals, Applications and Implementations
• CoT-Valve: Length-Compressible Chain-of-Thought Tuning
• Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making
• The Frontier of Data Erasure: A Survey on Machine Unlearning for Large Language Models
• Toward Democratized Generative AI in Next-Generation Mobile Edge Networks
• Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks
• Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances
• Semantic Alignment for Multimodal Large Language Models
• AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
• The Llama 3 Herd of Models
• Large Language Model (LLM)-enabled Graphs in Dynamic Networking
• TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models
• Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
• A Review of Safe Reinforcement Learning Methods for Modern Power Systems
• Large Language Model(LLM) assisted End-to-End Network Health Management based on Multi-Scale Semanticization
• Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
• LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning
• WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
• WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
• Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
• Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
• Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling
• ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge
• Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
• When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
• Large Language Model Enhanced Multi-Agent Systems for 6G Communications
• Large Language Models for Networking: Applications, Enabling Techniques, and Challenges
• Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
• Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
• A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication
• Eureka: Human-Level Reward Design via Coding Large Language Models
• LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
• Qwen Technical Report
• State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
• Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
• SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
• NExT-GPT: Any-to-Any Multimodal LLM
• ExpeL: LLM Agents Are Experiential Learners
• Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies in Zero Touch Networks
• Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
• Learning to Model the World with Language
• A Survey on Evaluation of Large Language Models
• A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity
• Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study
• Visual Instruction Tuning
• Self-Refine: Iterative Refinement with Self-Feedback
• GPT-4 Technical Report
• LLaMA: Open and Efficient Foundation Language Models
• Some Fundamental Aspects about Lipschitz Continuity of Neural Networks
• Guiding Pretraining in Reinforcement Learning with Large Language Models
• Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
• LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
• Deep Black-Box Reinforcement Learning with Movement Primitives
• Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications
• Training Compute-Optimal Large Language Models
• MIMO-GAN: Generative MIMO Channel Modeling
• Training language models to follow instructions with human feedback
• Pre-Trained Language Models for Interactive Decision-Making
• BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
• DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
• Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
• LoRA: Low-Rank Adaptation of Large Language Models
• The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
• Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled\n Wireless Networks: A Tutorial
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Improving Generalization in Reinforcement Learning with Mixture Regularization
• Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
• Language Models are Few-Shot Learners
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Explainable Reinforcement Learning: A Survey
• Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
• Scaling Laws for Neural Language Models
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks
• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
• Transparency and Explanation in Deep Reinforcement Learning Neural Networks
• Low-latency Networking: Where Latency Lurks and How to Tame It
• Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Proximal Policy Optimization Algorithms
• Attention Is All You Need
• Continuous control with deep reinforcement learning
• Playing Atari with Deep Reinforcement Learning
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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