@inproceedings{gulipilli-2026-extended,
title = "Extended-{SVD} Low-Rank Adaptation: {PLORA} for Parameter-Efficient Fine-Tuning",
author = "Gulipilli, Ratan Kumar",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.5/",
pages = "74--93",
ISBN = "979-8-89176-444-6",
abstract = "We introduce Extended-SVD Low-Rank Adaptation, a parameter-efficient framework that extends the pretrained singular subspace while retaining a frozen spectral coordinate system and fixed singular-value-derived scaling. Its stabilized variant, PLORA (Procrustes Low-Rank Adaptation), applies localized Procrustes-inspired reorientation to the extended basis and confines task-specific learning to compact routing matrices $P$, $R$, and $Q$.\par Across GLUE and vision benchmarks, PLORA achieves competitive performance with substantially fewer trainable parameters. In the full-network comparison, PLORA-r8 uses substantially fewer trainable parameters, although the performance gap varies by task. Beyond accuracy, PLORA produces task- and architecture-dependent changes in conditioning, effective rank, and subspace evolution.\par Ablations show that Random Bases produce poorer routing-core conditioning and markedly lower top-$r$ right-subspace overlap in the evaluated NLP tasks. In the evaluated settings, pretrained spectral bases are associated with more favorable optimization geometry, while Procrustes reorientation acts as an optimization-geometric regularizer. Overall, Extended-SVD provides a structured low-rank adaptation framework for studying the role of pretrained geometry in PEFT."
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<abstract>We introduce Extended-SVD Low-Rank Adaptation, a parameter-efficient framework that extends the pretrained singular subspace while retaining a frozen spectral coordinate system and fixed singular-value-derived scaling. Its stabilized variant, PLORA (Procrustes Low-Rank Adaptation), applies localized Procrustes-inspired reorientation to the extended basis and confines task-specific learning to compact routing matrices P, R, and Q.\par Across GLUE and vision benchmarks, PLORA achieves competitive performance with substantially fewer trainable parameters. In the full-network comparison, PLORA-r8 uses substantially fewer trainable parameters, although the performance gap varies by task. Beyond accuracy, PLORA produces task- and architecture-dependent changes in conditioning, effective rank, and subspace evolution.\par Ablations show that Random Bases produce poorer routing-core conditioning and markedly lower top-r right-subspace overlap in the evaluated NLP tasks. In the evaluated settings, pretrained spectral bases are associated with more favorable optimization geometry, while Procrustes reorientation acts as an optimization-geometric regularizer. Overall, Extended-SVD provides a structured low-rank adaptation framework for studying the role of pretrained geometry in PEFT.</abstract>
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%0 Conference Proceedings
%T Extended-SVD Low-Rank Adaptation: PLORA for Parameter-Efficient Fine-Tuning
%A Gulipilli, Ratan Kumar
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F gulipilli-2026-extended
%X We introduce Extended-SVD Low-Rank Adaptation, a parameter-efficient framework that extends the pretrained singular subspace while retaining a frozen spectral coordinate system and fixed singular-value-derived scaling. Its stabilized variant, PLORA (Procrustes Low-Rank Adaptation), applies localized Procrustes-inspired reorientation to the extended basis and confines task-specific learning to compact routing matrices P, R, and Q.\par Across GLUE and vision benchmarks, PLORA achieves competitive performance with substantially fewer trainable parameters. In the full-network comparison, PLORA-r8 uses substantially fewer trainable parameters, although the performance gap varies by task. Beyond accuracy, PLORA produces task- and architecture-dependent changes in conditioning, effective rank, and subspace evolution.\par Ablations show that Random Bases produce poorer routing-core conditioning and markedly lower top-r right-subspace overlap in the evaluated NLP tasks. In the evaluated settings, pretrained spectral bases are associated with more favorable optimization geometry, while Procrustes reorientation acts as an optimization-geometric regularizer. Overall, Extended-SVD provides a structured low-rank adaptation framework for studying the role of pretrained geometry in PEFT.
%U https://aclanthology.org/2026.findings-aacl.5/
%P 74-93
Markdown (Informal)
[Extended-SVD Low-Rank Adaptation: PLORA for Parameter-Efficient Fine-Tuning](https://aclanthology.org/2026.findings-aacl.5/) (Gulipilli, Findings 2026)
ACL