@inproceedings{zheng-etal-2026-decouple,
title = "Decouple and Orthogonalize: A Data-Free Framework for {L}o{RA} Merging",
author = "Zheng, Shenghe and
Wang, Hongzhi and
Huang, Chenyu and
Wang, Xiaohui and
Fan, Jiayuan and
Hu, Shuyue and
Ye, Peng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1516/",
doi = "10.18653/2026.findings-emnlp.1516",
pages = "32287--32307",
ISBN = "979-8-89176-462-0",
abstract = "With the growing availability of open-source task-specific models, model merging has become a practical way to combine multiple models into one. However, existing studies mainly focus on fully fine-tuned models and overlook widely used LoRA modules. We show that full fine-tuning merging methods perform poorly on LoRA due to its larger cross-task magnitude disparities, which distort merged parameters and degrade performance. To address this, we propose DO-Merging (Decouple and Orthogonalize), a data-free framework for LoRA merging. Specifically, we decompose model weights into magnitude and direction components and merge them independently, preventing magnitude imbalances from distorting the merged direction. We further introduce a layer-wise gradient descent method with orthogonal constraints that reduces inter-task interference among direction components without requiring any training data. We provide theoretical analysis supporting both components. Extensive experiments across vision, language, and multi-modal domains show that DO-Merging consistently outperforms existing methods, achieving up to 5{\%} gains on vision tasks, with negligible additional cost. Both parts can be integrated into existing merging methods as plug-and-play enhancements."
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<abstract>With the growing availability of open-source task-specific models, model merging has become a practical way to combine multiple models into one. However, existing studies mainly focus on fully fine-tuned models and overlook widely used LoRA modules. We show that full fine-tuning merging methods perform poorly on LoRA due to its larger cross-task magnitude disparities, which distort merged parameters and degrade performance. To address this, we propose DO-Merging (Decouple and Orthogonalize), a data-free framework for LoRA merging. Specifically, we decompose model weights into magnitude and direction components and merge them independently, preventing magnitude imbalances from distorting the merged direction. We further introduce a layer-wise gradient descent method with orthogonal constraints that reduces inter-task interference among direction components without requiring any training data. We provide theoretical analysis supporting both components. Extensive experiments across vision, language, and multi-modal domains show that DO-Merging consistently outperforms existing methods, achieving up to 5% gains on vision tasks, with negligible additional cost. Both parts can be integrated into existing merging methods as plug-and-play enhancements.</abstract>
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%0 Conference Proceedings
%T Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging
%A Zheng, Shenghe
%A Wang, Hongzhi
%A Huang, Chenyu
%A Wang, Xiaohui
%A Fan, Jiayuan
%A Hu, Shuyue
%A Ye, Peng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zheng-etal-2026-decouple
%X With the growing availability of open-source task-specific models, model merging has become a practical way to combine multiple models into one. However, existing studies mainly focus on fully fine-tuned models and overlook widely used LoRA modules. We show that full fine-tuning merging methods perform poorly on LoRA due to its larger cross-task magnitude disparities, which distort merged parameters and degrade performance. To address this, we propose DO-Merging (Decouple and Orthogonalize), a data-free framework for LoRA merging. Specifically, we decompose model weights into magnitude and direction components and merge them independently, preventing magnitude imbalances from distorting the merged direction. We further introduce a layer-wise gradient descent method with orthogonal constraints that reduces inter-task interference among direction components without requiring any training data. We provide theoretical analysis supporting both components. Extensive experiments across vision, language, and multi-modal domains show that DO-Merging consistently outperforms existing methods, achieving up to 5% gains on vision tasks, with negligible additional cost. Both parts can be integrated into existing merging methods as plug-and-play enhancements.
%R 10.18653/2026.findings-emnlp.1516
%U https://aclanthology.org/2026.findings-emnlp.1516/
%U https://doi.org/10.18653/2026.findings-emnlp.1516
%P 32287-32307
Markdown (Informal)
[Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging](https://aclanthology.org/2026.findings-emnlp.1516/) (Zheng et al., Findings 2026)
ACL