DeepSeek-Inspired Exploration of RL-Based LLMs and Synergy with Wireless Networks: A Survey

Recently, reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have attracted significant attention for their impressive capabilities in multimodal data understanding. Meanwhile, as information services rapidly expand, there is increasing demand for intelligent and adaptable wireless networks to support this growth. Integrating LLMs with wireless networks offers a promising solution, as LLMs enhance network optimization with strong reasoning and decision-making abilities, while wireless infrastructure enables broad deployment of these models. This synergy calls for deeper exploration into the convergence of these two fields. To this end, this article makes a timely contribution by first reviewing key technologies for wireless network optimization, establishing a foundation for understanding how LLMs can be effectively integrated. We then turn to recent advancements in RL-based LLMs, particularly the open-source DeepSeek models, as they offer greater accessibility and customizability for researchers and practitioners. Subsequently, we explore the synergy between these fields, emphasizing motivations, open challenges, and potential solutions. Finally, we provide insights into future directions and societal impacts. Overall, this survey offers a comprehensive exploration of the relationship between LLMs and wireless networks, presenting a vision of how these domains empower each other to drive innovation.