# Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization

arXiv: https://arxiv.org/abs/2602.13210 · HTML: https://mlsanity.com/paper/2602.13210/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods](https://mlsanity.com/paper/2404.00282/) (2024)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [LLM-Empowered State Representation for Reinforcement Learning](https://mlsanity.com/paper/2407.13237/) (2024)
- [LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2406.06059/) (2024)
- [Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs](https://mlsanity.com/paper/2407.06227/) (2024)
- [Unified Far-Field and Near-Field in Holographic MIMO: A Wavenumber-Domain Perspective](https://mlsanity.com/paper/2407.14815/) (2024)
- [Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method](https://mlsanity.com/paper/2501.01141/) (2025)
- [Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking](https://mlsanity.com/paper/2505.21045/) (2025)

## Built on by (most cited first, 1 of 1)

- [Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models](https://mlsanity.com/paper/2606.31334/) (2026)

## Abstract (arXiv)

Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex environments, leading to substantial computational demands, distributed intelligence, and potentially inconsistent outcomes. Large language models (LLMs), with their extensive pretrained knowledge and advanced reasoning capabilities, offer promising tools to enhance RL in optimizing 6G wireless networks. We explore RL models augmented by LLMs, emphasizing their roles and the potential benefits of their synergy in wireless network optimization. We then examine LLM-enabled RL across various protocol layers: physical, data link, network, transport, and application layers. Additionally, we propose an LLM-assisted state representation and semantic extraction to enhance the multi-agent reinforcement learning (MARL) framework. This approach is applied to service migration and request routing, as well as topology graph generation in unmanned aerial vehicle (UAV)-satellite networks. Through case studies, we demonstrate that our framework effectively performs optimization of wireless network. Finally, we outline prospective research directions for LLM-enabled RL in wireless network optimization.