AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
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. To address this, we first investigate how search depth affects accuracy and find a minimal sufficient search depth that defines an accuracy-efficiency trade-off, jointly determined by question complexity and the agent's capability. Furthermore, we ...
| Authors | Jingbo Sun, Wenyue Chong, Songjun Tu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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