@inproceedings{wang-etal-2025-hd,
title = "{HD}-{P}i{SSA}: High-Rank Distributed Orthogonal Adaptation",
author = "Wang, Yiding and
Meng, Fanxu and
Zhang, Xuefeng and
Jiang, Fan and
Tang, Pingzhi and
Zhang, Muhan",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.330/",
doi = "10.18653/v1/2025.emnlp-main.330",
pages = "6515--6528",
ISBN = "979-8-89176-332-6",
abstract = "Existing parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs), such as LoRA and PiSSA, constrain model updates to low-rank subspaces, limiting their expressiveness and leading to suboptimal performance on complex tasks. To address this, we introduce **H**igh-rank **D**istributed **PiSSA (HD-PiSSA)**, a distributed PEFT approach that initializes **orthogonal adapters** across different devices and aggregates their delta updates collectively on (W) for fine-tuning. Unlike Data Parallel LoRA or PiSSA, which maintain identical adapters across all devices, HD-PiSSA assigns different principal components of the pre-trained weights to each GPU, significantly expanding the range of update directions. This results in over 16{\texttimes} higher effective updated ranks than data-parallel LoRA or PiSSA when fine-tuning on 8 GPUs with the same per-device adapter rank. Empirically, HD-PiSSA benefits from this extra optimization flexibility and outperforms both LoRA and PiSSA across a variety of challenging downstream tasks, including mathematics, code, and multi-task learning."
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<abstract>Existing parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs), such as LoRA and PiSSA, constrain model updates to low-rank subspaces, limiting their expressiveness and leading to suboptimal performance on complex tasks. To address this, we introduce **H**igh-rank **D**istributed **PiSSA (HD-PiSSA)**, a distributed PEFT approach that initializes **orthogonal adapters** across different devices and aggregates their delta updates collectively on (W) for fine-tuning. Unlike Data Parallel LoRA or PiSSA, which maintain identical adapters across all devices, HD-PiSSA assigns different principal components of the pre-trained weights to each GPU, significantly expanding the range of update directions. This results in over 16× higher effective updated ranks than data-parallel LoRA or PiSSA when fine-tuning on 8 GPUs with the same per-device adapter rank. Empirically, HD-PiSSA benefits from this extra optimization flexibility and outperforms both LoRA and PiSSA across a variety of challenging downstream tasks, including mathematics, code, and multi-task learning.</abstract>
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%0 Conference Proceedings
%T HD-PiSSA: High-Rank Distributed Orthogonal Adaptation
%A Wang, Yiding
%A Meng, Fanxu
%A Zhang, Xuefeng
%A Jiang, Fan
%A Tang, Pingzhi
%A Zhang, Muhan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-hd
%X Existing parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs), such as LoRA and PiSSA, constrain model updates to low-rank subspaces, limiting their expressiveness and leading to suboptimal performance on complex tasks. To address this, we introduce **H**igh-rank **D**istributed **PiSSA (HD-PiSSA)**, a distributed PEFT approach that initializes **orthogonal adapters** across different devices and aggregates their delta updates collectively on (W) for fine-tuning. Unlike Data Parallel LoRA or PiSSA, which maintain identical adapters across all devices, HD-PiSSA assigns different principal components of the pre-trained weights to each GPU, significantly expanding the range of update directions. This results in over 16× higher effective updated ranks than data-parallel LoRA or PiSSA when fine-tuning on 8 GPUs with the same per-device adapter rank. Empirically, HD-PiSSA benefits from this extra optimization flexibility and outperforms both LoRA and PiSSA across a variety of challenging downstream tasks, including mathematics, code, and multi-task learning.
%R 10.18653/v1/2025.emnlp-main.330
%U https://aclanthology.org/2025.emnlp-main.330/
%U https://doi.org/10.18653/v1/2025.emnlp-main.330
%P 6515-6528
Markdown (Informal)
[HD-PiSSA: High-Rank Distributed Orthogonal Adaptation](https://aclanthology.org/2025.emnlp-main.330/) (Wang et al., EMNLP 2025)
ACL
- Yiding Wang, Fanxu Meng, Xuefeng Zhang, Fan Jiang, Pingzhi Tang, and Muhan Zhang. 2025. HD-PiSSA: High-Rank Distributed Orthogonal Adaptation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6515–6528, Suzhou, China. Association for Computational Linguistics.