Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911300225859584 |
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| author | Cai, Lingyi Fu, Wenjie Huang, Yuxi Zhang, Ruichen Liu, Yinqiu Kang, Jiawen Xiong, Zehui Jiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Shen, Xuemin |
| author_facet | Cai, Lingyi Fu, Wenjie Huang, Yuxi Zhang, Ruichen Liu, Yinqiu Kang, Jiawen Xiong, Zehui Jiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Shen, Xuemin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks Cai, Lingyi Fu, Wenjie Huang, Yuxi Zhang, Ruichen Liu, Yinqiu Kang, Jiawen Xiong, Zehui Jiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Shen, Xuemin Networking and Internet Architecture 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. |
| title | Tutorial on Large Language Model-Enhanced Reinforcement Learning for Wireless Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2512.03722 |