基于强化学习的智能体搜索综合综述：基础、作用、优化、评估与应用
A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications
AI总结：
该综述首次系统梳理基于强化学习的智能体搜索领域，从功能角色、优化策略、应用范围三维度总结方法、评估与应用，探讨挑战与未来方向，推动RL与智能体搜索融合研究。
摘要：
大语言模型（LLMs）的出现通过开放式自然语言交互变革了信息获取与推理方式。然而，LLMs仍受限于静态知识、事实幻觉，且无法检索实时或特定领域的信息。检索增强生成（RAG）通过将模型输出建立在外部证据基础上缓解了这些问题，但传统RAG流水线通常是单轮且启发式的，缺乏对检索和推理的自适应控制。近期智能体搜索的进展解决了这些局限，它使LLMs能够通过与搜索环境的多步交互进行规划、检索和反思。在这一范式中，强化学习（RL）为自适应且自我改进的搜索行为提供了强大机制。本综述首次全面概述了基于RL的智能体搜索，从三个互补维度梳理这一新兴领域：（i）RL的用途（功能角色），（ii）RL的使用方式（优化策略），以及（iii）RL的应用场景（优化范围）。我们总结了代表性方法、评估协议和应用，并讨论了构建可靠且可扩展的RL驱动智能体搜索系统面临的开放挑战和未来方向。我们希望本综述能激发未来关于RL与智能体搜索融合的研究。我们的资源库位于https://github.com/ventr1c/Awesome-RL-based-Agentic-Search-Papers。
英文摘要：
The advent of large language models (LLMs) has transformed information access and reasoning through open-ended natural language interaction. However, LLMs remain limited by static knowledge, factual hallucinations, and the inability to retrieve real-time or domain-specific information. Retrieval-Augmented Generation (RAG) mitigates these issues by grounding model outputs in external evidence, but traditional RAG pipelines are often single turn and heuristic, lacking adaptive control over retrieval and reasoning. Recent advances in agentic search address these limitations by enabling LLMs to plan, retrieve, and reflect through multi-step interaction with search environments. Within this paradigm, reinforcement learning (RL) offers a powerful mechanism for adaptive and self-improving search behavior. This survey provides the first comprehensive overview of \emph{RL-based agentic search}, organizing the emerging field along three complementary dimensions: (i) What RL is for (functional roles), (ii) How RL is used (optimization strategies), and (iii) Where RL is applied (scope of optimization). We summarize representative methods, evaluation protocols, and applications, and discuss open challenges and future directions toward building reliable and scalable RL driven agentic search systems. We hope this survey will inspire future research on the integration of RL and agentic search. Our repository is available at https://github.com/ventr1c/Awesome-RL-based-Agentic-Search-Papers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
