@inproceedings{ploner-akbik-2026-variable,
title = "Variable-Rank Adaptation: A Comprehensive Survey of Rank Budgeting {L}o{RA} Variants",
author = "Ploner, Max and
Akbik, Alan",
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.2430/",
pages = "52150--52172",
ISBN = "979-8-89176-462-0",
abstract = "The capacity of Low-Rank Adaptation (LoRA) is fundamentally governed by its rank parameter. While standard implementations apply a uniform rank across all layers, a recent wave of variable-rank extensions demonstrates that tailoring ranks to module importance improves expressiveness. However, this emerging literature remains highly fragmented and lacks systematic cross-comparisons. To unify the field, we propose a taxonomy grouping these approaches into four categories: Rank growth, pruning, redistribution, and pre-allocation. We analyze their design dimensions, theoretical expressiveness, implementation complexity, and memory efficiency. Finally, we outline critical challenges inhibiting adoption, including standardized evaluations and memory metrics."
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%0 Conference Proceedings
%T Variable-Rank Adaptation: A Comprehensive Survey of Rank Budgeting LoRA Variants
%A Ploner, Max
%A Akbik, Alan
%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 ploner-akbik-2026-variable
%X The capacity of Low-Rank Adaptation (LoRA) is fundamentally governed by its rank parameter. While standard implementations apply a uniform rank across all layers, a recent wave of variable-rank extensions demonstrates that tailoring ranks to module importance improves expressiveness. However, this emerging literature remains highly fragmented and lacks systematic cross-comparisons. To unify the field, we propose a taxonomy grouping these approaches into four categories: Rank growth, pruning, redistribution, and pre-allocation. We analyze their design dimensions, theoretical expressiveness, implementation complexity, and memory efficiency. Finally, we outline critical challenges inhibiting adoption, including standardized evaluations and memory metrics.
%U https://aclanthology.org/2026.findings-emnlp.2430/
%P 52150-52172
Markdown (Informal)
[Variable-Rank Adaptation: A Comprehensive Survey of Rank Budgeting LoRA Variants](https://aclanthology.org/2026.findings-emnlp.2430/) (Ploner & Akbik, Findings 2026)
ACL