@inproceedings{liu-etal-2026-code,
title = "{C}o{D}e-{L}o{RA}: Mitigating the Orthogonality Dilemma in Continual Learning of {LLM}s via Knowledge Consolidation and Decoupling",
author = "Liu, Maoqi and
Fang, Quan and
He, Yufei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1188/",
pages = "27223--27239",
ISBN = "979-8-89176-459-0",
abstract = "Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, recent advances implement low-rank adaptation with orthogonal projections (e.g., O-LoRA) to isolate task parameters. However, we reveal that such strict geometric constraints trigger an ``Orthogonality Dilemma'': rigid parameter isolation impedes the transfer and accumulation of shared representations across semantically related tasks. In this work, we propose a new replay-free method, called $\underline{\mathit{Co}}\mathit{nsolidation}$ and $\underline{\mathit{De}}\mathit{coupling}$ $\underline{\mathit{LoRA}}$ (CoDe-LoRA), for CL of LLMs. CoDe-LoRA disentangles the learning process into $\mathit{Consolidating\ Universal\ Knowledge}$ and $\mathit{Decoupling\ Task\text{-}Specific\ Knowledge}$. To achieve this, CoDe-LoRA leverages an adaptive null-space projection mechanism and semantic routing to balance knowledge accumulation with task-specific adaptation. Experimental results on diverse CL benchmarks demonstrate that CoDe-LoRA consistently outperforms state-of-the-art methods. Our code is available at [https://github.com/Estrellajer/CoDe-LoRA](https://github.com/Estrellajer/CoDe-LoRA)."
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<abstract>Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, recent advances implement low-rank adaptation with orthogonal projections (e.g., O-LoRA) to isolate task parameters. However, we reveal that such strict geometric constraints trigger an “Orthogonality Dilemma”: rigid parameter isolation impedes the transfer and accumulation of shared representations across semantically related tasks. In this work, we propose a new replay-free method, called \underline\mathitCo\mathitnsolidation and \underline\mathitDe\mathitcoupling \underline\mathitLoRA (CoDe-LoRA), for CL of LLMs. CoDe-LoRA disentangles the learning process into \mathitConsolidating Universal Knowledge and \mathitDecoupling Task\text-Specific Knowledge. To achieve this, CoDe-LoRA leverages an adaptive null-space projection mechanism and semantic routing to balance knowledge accumulation with task-specific adaptation. Experimental results on diverse CL benchmarks demonstrate that CoDe-LoRA consistently outperforms state-of-the-art methods. Our code is available at [https://github.com/Estrellajer/CoDe-LoRA](https://github.com/Estrellajer/CoDe-LoRA).</abstract>
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%0 Conference Proceedings
%T CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling
%A Liu, Maoqi
%A Fang, Quan
%A He, Yufei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-code
%X Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, recent advances implement low-rank adaptation with orthogonal projections (e.g., O-LoRA) to isolate task parameters. However, we reveal that such strict geometric constraints trigger an “Orthogonality Dilemma”: rigid parameter isolation impedes the transfer and accumulation of shared representations across semantically related tasks. In this work, we propose a new replay-free method, called \underline\mathitCo\mathitnsolidation and \underline\mathitDe\mathitcoupling \underline\mathitLoRA (CoDe-LoRA), for CL of LLMs. CoDe-LoRA disentangles the learning process into \mathitConsolidating Universal Knowledge and \mathitDecoupling Task\text-Specific Knowledge. To achieve this, CoDe-LoRA leverages an adaptive null-space projection mechanism and semantic routing to balance knowledge accumulation with task-specific adaptation. Experimental results on diverse CL benchmarks demonstrate that CoDe-LoRA consistently outperforms state-of-the-art methods. Our code is available at [https://github.com/Estrellajer/CoDe-LoRA](https://github.com/Estrellajer/CoDe-LoRA).
%U https://aclanthology.org/2026.emnlp-main.1188/
%P 27223-27239
Markdown (Informal)
[CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling](https://aclanthology.org/2026.emnlp-main.1188/) (Liu et al., EMNLP 2026)
ACL