@inproceedings{wang-etal-2025-continual,
title = "Continual Gradient Low-Rank Projection Fine-Tuning for {LLM}s",
author = "Wang, Chenxu and
Lyu, Yilin and
Sun, Zicheng and
Jing, Liping",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.721/",
doi = "10.18653/v1/2025.acl-long.721",
pages = "14815--14829",
ISBN = "979-8-89176-251-0",
abstract = "Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model{'}s ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints. We propose GORP ($\underline{\textbf{G}}$radient L$\underline{\textbf{O}}$w $\underline{\textbf{R}}$ank $\underline{\textbf{P}}$rojection) for Continual Learning, a novel training strategy that overcomes these limitations by synergistically combining full and low-rank parameters and jointly updating within a unified low-rank gradient subspace. GORP expands the optimization space while preserving efficiency and mitigating catastrophic forgetting. Extensive experiments on continual learning benchmarks demonstrate GORP{'}s superior performance compared to existing state-of-the-art approaches. Code is available at https://github.com/Wcxwcxw/GORP."
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<abstract>Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model’s ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints. We propose GORP (\underlineGradient L\underlineOw \underlineRank \underlineProjection) for Continual Learning, a novel training strategy that overcomes these limitations by synergistically combining full and low-rank parameters and jointly updating within a unified low-rank gradient subspace. GORP expands the optimization space while preserving efficiency and mitigating catastrophic forgetting. Extensive experiments on continual learning benchmarks demonstrate GORP’s superior performance compared to existing state-of-the-art approaches. Code is available at https://github.com/Wcxwcxw/GORP.</abstract>
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%0 Conference Proceedings
%T Continual Gradient Low-Rank Projection Fine-Tuning for LLMs
%A Wang, Chenxu
%A Lyu, Yilin
%A Sun, Zicheng
%A Jing, Liping
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-continual
%X Continual fine-tuning of Large Language Models (LLMs) is hampered by the trade-off between efficiency and expressiveness. Low-Rank Adaptation (LoRA) offers efficiency but constrains the model’s ability to learn new tasks and transfer knowledge due to its low-rank nature and reliance on explicit parameter constraints. We propose GORP (\underlineGradient L\underlineOw \underlineRank \underlineProjection) for Continual Learning, a novel training strategy that overcomes these limitations by synergistically combining full and low-rank parameters and jointly updating within a unified low-rank gradient subspace. GORP expands the optimization space while preserving efficiency and mitigating catastrophic forgetting. Extensive experiments on continual learning benchmarks demonstrate GORP’s superior performance compared to existing state-of-the-art approaches. Code is available at https://github.com/Wcxwcxw/GORP.
%R 10.18653/v1/2025.acl-long.721
%U https://aclanthology.org/2025.acl-long.721/
%U https://doi.org/10.18653/v1/2025.acl-long.721
%P 14815-14829
Markdown (Informal)
[Continual Gradient Low-Rank Projection Fine-Tuning for LLMs](https://aclanthology.org/2025.acl-long.721/) (Wang et al., ACL 2025)
ACL
- Chenxu Wang, Yilin Lyu, Zicheng Sun, and Liping Jing. 2025. Continual Gradient Low-Rank Projection Fine-Tuning for LLMs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14815–14829, Vienna, Austria. Association for Computational Linguistics.