# AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2406.19251 · HTML: https://mlsanity.com/paper/2406.19251/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing](https://mlsanity.com/paper/2404.14618/) (2024)
- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://mlsanity.com/paper/2403.12968/) (2024)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://mlsanity.com/paper/2303.04673/) (2023)
- [An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models](https://mlsanity.com/paper/2106.09204/) (2021)

## Built on by (most cited first, 13 of 13)

- [METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation](https://mlsanity.com/paper/2412.10543/) (2024)
- [Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning](https://mlsanity.com/paper/2505.24478/) (2025)
- [AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces](https://mlsanity.com/paper/2608.23041/) (2026)
- [Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems](https://mlsanity.com/paper/2502.18635/) (2025)
- [Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents](https://mlsanity.com/paper/2505.12065/) (2025)
- [Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning](https://mlsanity.com/paper/2501.07063/) (2025)
- [AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines](https://mlsanity.com/paper/2605.02967/) (2026)
- [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026)
- [A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content](https://mlsanity.com/paper/2504.16120/) (2025)
- [Multi-Armed Bandits-Based Optimization of Decision Trees](https://mlsanity.com/paper/2508.05957/) (2025)
- [Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring](https://mlsanity.com/paper/2608.23814/) (2026)
- [Bandits in Prod: Hyperparameter Optimization at Inference Time](https://mlsanity.com/paper/2609.01335/) (2026)
- [UniK: Universal Knowledge Perception for Digital and Physical AI](https://mlsanity.com/paper/2609.23971/) (2026)

## Abstract (arXiv)

Recent advancements in Large Language Models have transformed ML/AI development, necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation (RAG) systems. To address the challenges of hyper-parameter optimization and online adaptation in RAG, we propose the AutoRAG-HP framework, which formulates the hyper-parameter tuning as an online multi-armed bandit (MAB) problem and introduces a novel two-level Hierarchical MAB (Hier-MAB) method for efficient exploration of large search spaces. We conduct extensive experiments on tuning hyper-parameters, such as top-k retrieved documents, prompt compression ratio, and embedding methods, using the ALCE-ASQA and Natural Questions datasets. Our evaluation from jointly optimization all three hyper-parameters demonstrate that MAB-based online learning methods can achieve Recall@5 ≈ 0.8 for scenarios with prominent gradients in search space, using only ~20% of the LLM API calls required by the Grid Search approach. Additionally, the proposed Hier-MAB approach outperforms other baselines in more challenging optimization scenarios. The code will be made available at https://aka.ms/autorag.