AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning

ArXi:2604.17337v1 Announce Type: new Agentic retrieval-augmented generation (RAG) systems enable large language models (LLMs) to solve complex tasks through multi-step interaction with external retrieval tools. However, such multi-step interaction often involves redundant search steps, incurring substantial computational cost and latency. Prior work limits search depth (i.e., the number of search steps) to reduce cost, but this often leads to underexploration of complex questions.