@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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.330/",
    pages = "6526--6539",
    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."
}