AI RESEARCH

CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG

arXiv CS.AI

ArXi:2605.08333v1 Announce Type: cross Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries is a challenging task due to the complex interactions and expensive evaluation costs. Existing algorithms are ineffective and slow in convergence, since they often treat RAG as a monolithic black box or only optimize partial hyperparameters. In this paper, we propose CDS4RAG, a framework that optimizes the full RAG hyperparameters using given queries via a new cyclic dual-sequential formulation.