Recent breakthroughs in large language models (LLMs), particularly in
reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to
unprecedented levels. By synergizing retrieval mechanisms with advanced
reasoning, LLMs can now tackle increasingly complex problems. This paper
presents a systematic review of the collaborative interplay between RAG and
reasoning, clearly defining “reasoning” within the RAG context. It construct a
comprehensive taxonomy encompassing multi-dimensional collaborative objectives,
representative paradigms, and technical implementations, and analyze the
bidirectional synergy methods. Additionally, we critically evaluate current
limitations in RAG assessment, including the absence of intermediate
supervision for multi-step reasoning and practical challenges related to
cost-risk trade-offs. To bridge theory and practice, we provide practical
guidelines tailored to diverse real-world applications. Finally, we identify
promising research directions, such as graph-based knowledge integration,
hybrid model collaboration, and RL-driven optimization. Overall, this work
presents a theoretical framework and practical foundation to advance RAG
systems in academia and industry, fostering the next generation of RAG
solutions.

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