@inproceedings{xiong-etal-2026-g,
title = "{G}-{L}o{RA}: Global-Local Decoupled Low-Rank Adaptation",
author = "Xiong, Jiahao and
Huang, Yihong and
Liu, Yihe and
Hu, Xianming and
Zhao, Hongbo and
Zhang, Kai",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1005/",
pages = "20125--20141",
ISBN = "979-8-89176-395-1",
abstract = "Low-Rank Adaptation (LoRA) has achieved remarkable progress in improving the fine-tuning efficiency and downstream performance of large language models (LLMs). Although prior work has recognized that different weight update matrices $\Delta \mathbf{W}$ exhibit varying importance and therefore should be allocated different ranks, parameters within the same update matrix are still typically constrained to a uniform rank configuration, neglecting fine-grained parameter-level heterogeneity. To address this limitation, we propose G-LoRA (Global-Local Decoupled LoRA), which decomposes each update matrix into global and local adapters. The key idea is to reorganize the rows and columns of the update matrix using a first-order Taylor approximation of parameter importance, such that highly influential parameters are clustered into a local sub-block of $\Delta \mathbf{W}$. During training, the local adapter then focuses on this high-importance sub-region and is allocated a higher rank, whereas the global adapter captures the residual updates for the entire update matrix with relatively lower rank. By allocating higher representational capacity to more critical parameters, G-LoRA enables more efficient utilization of model resources. Extensive evaluations on benchmarks spanning commonsense reasoning, mathematical reasoning, and code generation demonstrate that G-LoRA achieves up to 2.7{\%} absolute accuracy improvement over LoRA and its variants, validating its effectiveness for LLM fine-tuning."
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<abstract>Low-Rank Adaptation (LoRA) has achieved remarkable progress in improving the fine-tuning efficiency and downstream performance of large language models (LLMs). Although prior work has recognized that different weight update matrices Δ \mathbfW exhibit varying importance and therefore should be allocated different ranks, parameters within the same update matrix are still typically constrained to a uniform rank configuration, neglecting fine-grained parameter-level heterogeneity. To address this limitation, we propose G-LoRA (Global-Local Decoupled LoRA), which decomposes each update matrix into global and local adapters. The key idea is to reorganize the rows and columns of the update matrix using a first-order Taylor approximation of parameter importance, such that highly influential parameters are clustered into a local sub-block of Δ \mathbfW. During training, the local adapter then focuses on this high-importance sub-region and is allocated a higher rank, whereas the global adapter captures the residual updates for the entire update matrix with relatively lower rank. By allocating higher representational capacity to more critical parameters, G-LoRA enables more efficient utilization of model resources. Extensive evaluations on benchmarks spanning commonsense reasoning, mathematical reasoning, and code generation demonstrate that G-LoRA achieves up to 2.7% absolute accuracy improvement over LoRA and its variants, validating its effectiveness for LLM fine-tuning.</abstract>
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%0 Conference Proceedings
%T G-LoRA: Global-Local Decoupled Low-Rank Adaptation
%A Xiong, Jiahao
%A Huang, Yihong
%A Liu, Yihe
%A Hu, Xianming
%A Zhao, Hongbo
%A Zhang, Kai
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xiong-etal-2026-g
%X Low-Rank Adaptation (LoRA) has achieved remarkable progress in improving the fine-tuning efficiency and downstream performance of large language models (LLMs). Although prior work has recognized that different weight update matrices Δ \mathbfW exhibit varying importance and therefore should be allocated different ranks, parameters within the same update matrix are still typically constrained to a uniform rank configuration, neglecting fine-grained parameter-level heterogeneity. To address this limitation, we propose G-LoRA (Global-Local Decoupled LoRA), which decomposes each update matrix into global and local adapters. The key idea is to reorganize the rows and columns of the update matrix using a first-order Taylor approximation of parameter importance, such that highly influential parameters are clustered into a local sub-block of Δ \mathbfW. During training, the local adapter then focuses on this high-importance sub-region and is allocated a higher rank, whereas the global adapter captures the residual updates for the entire update matrix with relatively lower rank. By allocating higher representational capacity to more critical parameters, G-LoRA enables more efficient utilization of model resources. Extensive evaluations on benchmarks spanning commonsense reasoning, mathematical reasoning, and code generation demonstrate that G-LoRA achieves up to 2.7% absolute accuracy improvement over LoRA and its variants, validating its effectiveness for LLM fine-tuning.
%U https://aclanthology.org/2026.findings-acl.1005/
%P 20125-20141
Markdown (Informal)
[G-LoRA: Global-Local Decoupled Low-Rank Adaptation](https://aclanthology.org/2026.findings-acl.1005/) (Xiong et al., Findings 2026)
ACL
- Jiahao Xiong, Yihong Huang, Yihe Liu, Xianming Hu, Hongbo Zhao, and Kai Zhang. 2026. G-LoRA: Global-Local Decoupled Low-Rank Adaptation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20125–20141, San Diego, California, United States. Association for Computational Linguistics.