Towards Symmetric Low-Rank Adapters
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917986601795584 |
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| author | Panoutsos, Tales Santos, Rodrygo L. T. Figueiredo, Flavio |
| author_facet | Panoutsos, Tales Santos, Rodrygo L. T. Figueiredo, Flavio |
| contents | In this paper, we introduce Symmetric Low-Rank Adapters, an optimized variant of LoRA with even fewer weights. This method utilizes Low-Rank Symmetric Weight Matrices to learn downstream tasks more efficiently. Traditional LoRA accumulates fine-tuning weights with the original pre-trained weights via a Singular Value Decomposition (SVD) like approach, i.e., model weights are fine-tuned via updates of the form $BA$ (where $B \in \mathbb{R}^{n\times r}$, $A \in \mathbb{R}^{r\times n}$, and $r$ is the rank of the merged weight matrix). In contrast, our approach, named SymLoRA, represents fine-tuning weights as a Spectral Decomposition, i.e., $Q \, diag(Λ)\, Q^T$, where $Q \in \mathbb{R}^{n\times r}$ and $Λ\in \mathbb{R}^r$. SymLoRA requires approximately half of the finetuning weights. Here, we show that this approach has negligible losses in downstream efficacy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Symmetric Low-Rank Adapters Panoutsos, Tales Santos, Rodrygo L. T. Figueiredo, Flavio Machine Learning Artificial Intelligence In this paper, we introduce Symmetric Low-Rank Adapters, an optimized variant of LoRA with even fewer weights. This method utilizes Low-Rank Symmetric Weight Matrices to learn downstream tasks more efficiently. Traditional LoRA accumulates fine-tuning weights with the original pre-trained weights via a Singular Value Decomposition (SVD) like approach, i.e., model weights are fine-tuned via updates of the form $BA$ (where $B \in \mathbb{R}^{n\times r}$, $A \in \mathbb{R}^{r\times n}$, and $r$ is the rank of the merged weight matrix). In contrast, our approach, named SymLoRA, represents fine-tuning weights as a Spectral Decomposition, i.e., $Q \, diag(Λ)\, Q^T$, where $Q \in \mathbb{R}^{n\times r}$ and $Λ\in \mathbb{R}^r$. SymLoRA requires approximately half of the finetuning weights. Here, we show that this approach has negligible losses in downstream efficacy. |
| title | Towards Symmetric Low-Rank Adapters |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.03719 |