---
title: "Who cites ReLoRA: High-Rank Training — 64 citing papers"
description: "64 papers of the Elamur Research library cite «ReLoRA: High-Rank Training Through Low-Rank Updates», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ReLoRA: High-Rank Training Through Low-Rank…](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/) / cited by

# Who cites «ReLoRA: High-Rank Training Through Low-Rank Updates»

64 papers of our library cite it (of the 141,840 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «ReLoRA: High-Rank Training Through Low-Rank Updates»](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/)

1. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
2. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)

62 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/) — ReLoRA: High-Rank Training Through Low-Rank Updates