针对RAG模型超参数优化难的问题，提出循环双序列优化框架，提升生成质量与效率。
CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
- 分步循环优化检索器与生成器的超参数，避免整体黑箱处理
- 在4个基准上21/24次超越基础算法，生成质量最高提升1.54倍
- 适用于多种算法，适合需要高效调优RAG系统的研究者
检索增强生成（RAG）对检索器和生成器的大量超参数敏感，但现有方法因复杂交互和高评估成本，常将RAG视为整体黑箱或仅优化部分参数，导致收敛慢且效果差。本文提出CDS4RAG框架，通过新的循环双序列范式，在给定查询下联合优化完整RAG超参数。该方法区分检索器与生成器的超参数，循环交替优化，并支持周期内精细预算分配与跨周期种子初始化，显著加速生成器优化。该框架具备算法无关性，可适配多种通用优化算法。在四个常用基准和两个主流大模型上的实验表明，CDS4RAG在21/24种情况下优于基线算法，所有场景均超越当前最优方法，生成质量最高提升1.54倍，同时实现更好加速比。
原文摘要 · Abstract (English)
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. CDS4RAG is special in the sense that it distinguishes the hyperparameters of the retriever and generator, cyclically optimizing them in turn. Such a paradigm allows us to design fine-grained within-cycle budget provision and expedite the optimization via cross-cycle seeding when optimizing the generator. CDS4RAG is also an algorithm-agnostic framework that can be paired with diverse general algorithms. Through experiments on four common benchmarks and two backbone LLMs, we reveal that CDS4RAG considerably boosts the vanilla algorithms in 21/24 cases while significantly outperforming state-of-the-art algorithms in all cases with up to 1.54x improvements of generation quality and better speedup.
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