# Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

arXiv: https://arxiv.org/abs/2501.16372 · HTML: https://mlsanity.com/paper/2501.16372/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models](https://mlsanity.com/paper/2410.03750/) (2024)

## Built on by (most cited first, 4 of 4)

- [FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation](https://mlsanity.com/paper/2508.20295/) (2025)
- [Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning](https://mlsanity.com/paper/2606.07500/) (2026)
- [Split-on-Share: Mixture of Sparse Experts for Task-Agnostic Continual Learning](https://mlsanity.com/paper/2601.17616/) (2026)
- [HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search](https://mlsanity.com/paper/2605.04103/) (2026)

## Abstract (arXiv)

The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This retrospective paper comprehensively discusses innovative approaches that synergize low-rank representations with Neural Architecture Search (NAS) techniques, particularly weight-sharing super-networks. Robust solutions for compressing and fine-tuning large pre-trained models are developed by integrating these methodologies. Our analysis highlights the potential of these combined strategies to democratize the use of LLMs, making them more accessible for deployment in resource-constrained environments. The resulting models exhibit reduced memory footprints and faster inference times, paving the way for more practical and scalable applications of LLMs. Models and code are available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.