https://arxiv.org/abs/2410.21228 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2410.21228 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2410.21228 (cs) [Submitted on 28 Oct 2024] Title:LoRA vs Full Fine-tuning: An Illusion of Equivalence Authors:Reece Shuttleworth, Jacob Andreas, Antonio Torralba, Pratyusha Sharma View a PDF of the paper titled LoRA vs Full Fine-tuning: An Illusion of Equivalence, by Reece Shuttleworth and 3 other authors View PDF HTML (experimental) Abstract:Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA) have been shown to match the performance of fully fine-tuned models on various tasks with an extreme reduction in the number of trainable parameters. Even in settings where both methods learn similarly accurate models, \ emph{are their learned solutions really equivalent?} We study how different fine-tuning methods change pre-trained models by analyzing the model's weight matrices through the lens of their spectral properties. We find that full fine-tuning and LoRA yield weight matrices whose singular value decompositions exhibit very different structure; moreover, the fine-tuned models themselves show distinct generalization behaviors when tested outside the adaptation task's distribution. More specifically, we first show that the weight matrices trained with LoRA have new, high-ranking singular vectors, which we call \emph{intruder dimensions}. Intruder dimensions do not appear during full fine-tuning. Second, we show that LoRA models with intruder dimensions, despite achieving similar performance to full fine-tuning on the target task, become worse models of the pre-training distribution and adapt less robustly to multiple tasks sequentially. Higher-rank, rank-stabilized LoRA models closely mirror full fine-tuning, even when performing on par with lower-rank LoRA models on the same tasks. These results suggest that models updated with LoRA and full fine-tuning access different parts of parameter space, even when they perform equally on the fine-tuned distribution. We conclude by examining why intruder dimensions appear in LoRA fine-tuned models, why they are undesirable, and how their effects can be minimized. Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2410.21228 [cs.LG] (or arXiv:2410.21228v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2410.21228 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Reece Shuttleworth [view email] [v1] Mon, 28 Oct 2024 17:14:01 UTC (9,438 KB) Full-text links: Access Paper: View a PDF of the paper titled LoRA vs Full Fine-tuning: An Illusion of Equivalence, by Reece Shuttleworth and 3 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats license icon view license Current browse context: cs.LG < prev | next > new | recent | 2024-10 Change to browse by: cs cs.CL References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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