@inproceedings{igor-etal-2026-simplexlora,
title = "{S}implex{L}o{RA}: Dynamic Rank Updating via Alternating Minimization with Simplex Projection",
author = "Igor, Shalygin and
Davydenko, Grigorii and
Veprikov, Andrey and
Antonov, Aleksey and
Bushkov, Nikolay and
Moiseev, Stanislav and
Beznosikov, Aleksandr",
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.2409/",
doi = "10.18653/2026.findings-emnlp.2409",
pages = "51747--51756",
ISBN = "979-8-89176-462-0",
abstract = "The success of large language models in real-world applications relies on parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which inserts compact, trainable adapters into selected layers. In this paper, we propose SimplexLoRA, a LoRA variant that adaptively adjusts adapter ranks during fine-tuning based on their learned importance. Unlike standard LoRA, which employs a fixed rank across all layers, SimplexLoRA optimizes per-adapter importance weights on the n-scaled simplex during a short warm-up and then freezes them, dynamically allocating capacity where it is most beneficial without increasing the overall parameter budget or inference cost. We evaluate SimplexLoRA across 12 language understanding, summarization, and reasoning tasks using DeBERTaV3, BART-large, Llama 3.1 8B, and Qwen3-8B. Our results show that SimplexLoRA consistently matches or outperforms standard LoRA and recent adaptive baselines, with the largest gains at low ranks. Our code is available at https://github.com/brain-lab-research/SimplexLoRA."
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<abstract>The success of large language models in real-world applications relies on parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which inserts compact, trainable adapters into selected layers. In this paper, we propose SimplexLoRA, a LoRA variant that adaptively adjusts adapter ranks during fine-tuning based on their learned importance. Unlike standard LoRA, which employs a fixed rank across all layers, SimplexLoRA optimizes per-adapter importance weights on the n-scaled simplex during a short warm-up and then freezes them, dynamically allocating capacity where it is most beneficial without increasing the overall parameter budget or inference cost. We evaluate SimplexLoRA across 12 language understanding, summarization, and reasoning tasks using DeBERTaV3, BART-large, Llama 3.1 8B, and Qwen3-8B. Our results show that SimplexLoRA consistently matches or outperforms standard LoRA and recent adaptive baselines, with the largest gains at low ranks. Our code is available at https://github.com/brain-lab-research/SimplexLoRA.</abstract>
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%0 Conference Proceedings
%T SimplexLoRA: Dynamic Rank Updating via Alternating Minimization with Simplex Projection
%A Igor, Shalygin
%A Davydenko, Grigorii
%A Veprikov, Andrey
%A Antonov, Aleksey
%A Bushkov, Nikolay
%A Moiseev, Stanislav
%A Beznosikov, Aleksandr
%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 igor-etal-2026-simplexlora
%X The success of large language models in real-world applications relies on parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which inserts compact, trainable adapters into selected layers. In this paper, we propose SimplexLoRA, a LoRA variant that adaptively adjusts adapter ranks during fine-tuning based on their learned importance. Unlike standard LoRA, which employs a fixed rank across all layers, SimplexLoRA optimizes per-adapter importance weights on the n-scaled simplex during a short warm-up and then freezes them, dynamically allocating capacity where it is most beneficial without increasing the overall parameter budget or inference cost. We evaluate SimplexLoRA across 12 language understanding, summarization, and reasoning tasks using DeBERTaV3, BART-large, Llama 3.1 8B, and Qwen3-8B. Our results show that SimplexLoRA consistently matches or outperforms standard LoRA and recent adaptive baselines, with the largest gains at low ranks. Our code is available at https://github.com/brain-lab-research/SimplexLoRA.
%R 10.18653/2026.findings-emnlp.2409
%U https://aclanthology.org/2026.findings-emnlp.2409/
%U https://doi.org/10.18653/2026.findings-emnlp.2409
%P 51747-51756
Markdown (Informal)
[SimplexLoRA: Dynamic Rank Updating via Alternating Minimization with Simplex Projection](https://aclanthology.org/2026.findings-emnlp.2409/) (Igor et al., Findings 2026)
ACL