Large Language Models (LLMs) excel at natural language understanding and generation, yet their reliance on static pre-training corpora may lead to outdated knowledge, hallucinations, and limited adaptability. Retrieval-Augmented Generation (RAG) mitigates these issues by grounding model outputs with external retrieval, but conventional RAG remains constrained by a fixed retrieve-then-generate routine and struggles with multi-step reasoning and tool calls. **Agentic RAG** addresses these limitations by enabling LLM agents to actively decompose tasks, issue exploratory queries, and refine evidence through iterative retrieval. Despite growing interest, the development of Agentic RAG is impeded by *data scarcity*: unlike traditional RAG, it requires challenging tasks that require planning, retrieval, and multiple reasoning decisions, and corresponding rich, interactive agent trajectories. This survey presents the first data-centric overview of Agentic RAG, framing its data lifecycle—data collecting, data preprocessing and task formulation, task construction, data for evaluation, and data enhancement for training—and cataloging representative training datasets and benchmarks in different domains (e.g. question answering, web, software engineering). From data perspectives, we aim to guide the creation of scalable, high-quality datasets for the next generation of adaptive, knowledge-seeking LLM agents. The project page is at https://github.com/fatty-belly/Awesome-AgenticRAG-Data/.

While retrieval techniques are widely used in practice, they still face significant challenges in cross-domain scenarios. Recently, generation-augmented methods have emerged as a promising solution to this problem. These methods enhance raw queries by incorporating additional information from an LLM-based generator, facilitating more direct retrieval of relevant documents. However, existing methods struggle with highly specialized situations that require extensive domain expertise. To address this problem, we present Reinforced-IR, a novel approach that jointly adapts a pre-trained retriever and generator for precise cross-domain retrieval. A key innovation of Reinforced-IR is its Self-Boosting framework, which enables retriever and generator to learn from each other’s feedback. Specifically, the generator is reinforced to generate query augmentations that enhance the retriever’s performance, while the retriever is trained to better discriminate the relevant documents identified by the generator. This iterative process allows the end-to-end retrieval performance to be progressively optimized using an unlabeled corpus from the target domain. In our experiment, Reinforced-IR outperforms existing domain adaptation methods by a large margin, leading to substantial improvements in retrieval quality across a wide range of application scenarios.We have publicly released our code at this repo.