CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
Researchers Pengzhou Chen and Tao Chen have introduced CDS4RAG, a novel framework designed to optimize the complex hyperparameters of Retrieval-Augmented Generation (RAG) systems. Published on arXiv in May 2026, this study addresses the inefficiencies of existing algorithms that often treat RAG as a monolithic black box or only partially optimize parameters. CDS4RAG employs a cyclic dual-sequential formulation that distinguishes between retriever and generator hyperparameters, optimizing them in turn. This approach enables fine-grained budget provision within cycles and accelerates optimization through cross-cycle seeding. The framework is algorithm-agnostic, allowing integration with various general algorithms. Experimental results across four common benchmarks and two backbone Large Language Models demonstrate that CDS4RAG significantly outperforms state-of-the-art methods in all tested cases. It achieved up to a 1.54x improvement in generation quality and offered better speedup, while also boosting vanilla algorithms in 21 out of 24 cases. This development marks a significant advancement in enhancing the efficiency and effectiveness of RAG systems by resolving challenges related to expensive evaluation costs and complex parameter interactions.
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CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
Researchers Pengzhou Chen and Tao Chen have introduced CDS4RAG, a novel framework designed to optimize the complex hyperparameters of Retrieval-Augmented Generation (RAG) systems. Published on arXiv in May 2026, this study addresses the inefficiencies of existing algorithms that often treat RAG as a monolithic black box or only partially optimize parameters. CDS4RAG employs a cyclic dual-sequential formulation that distinguishes between retriever and generator hyperparameters, optimizing them in turn. This approach enables fine-grained budget provision within cycles and accelerates optimization through cross-cycle seeding. The framework is algorithm-agnostic, allowing integration with various general algorithms. Experimental results across four common benchmarks and two backbone Large Language Models demonstrate that CDS4RAG significantly outperforms state-of-the-art methods in all tested cases. It achieved up to a 1.54x improvement in generation quality and offered better speedup, while also boosting vanilla algorithms in 21 out of 24 cases. This development marks a significant advancement in enhancing the efficiency and effectiveness of RAG systems by resolving challenges related to expensive evaluation costs and complex parameter interactions.
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