A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications

A recent survey highlights the advancements in reinforcement learning-based agentic search, particularly in the context of large language models (LLMs). These models have revolutionized how we access and reason about information, yet they face challenges like static knowledge and factual inaccuracies. The introduction of Retrieval-Augmented Generation (RAG) offers a promising solution by enhancing model outputs with real-time, domain-specific data. This development is significant as it not only improves the reliability of information retrieval but also opens new avenues for applications across various fields.