OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation

• URL: http://arxiv.org/abs/2505.14350v2
• Date: Wed, 21 May 2025 04:25:30 GMT
• Title: OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation
• Authors: Jialong Han, Si Zhang, Ke Zhang,
• Abstract summary: We present OSoRA, a novel PEFT method for Large Language Models (LLMs)<n>OSoRA substantially reduces computational resource requirements by minimizing the number of trainable parameters during fine-tuning.<n> Comprehensive evaluations across mathematical reasoning, common sense reasoning, and other benchmarks demonstrate that OSoRA achieves comparable or superior performance to state-of-the-art methods.
• Score: 9.048461365342204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning Large Language Models (LLMs) has become increasingly challenging due to their massive scale and associated computational costs. Parameter-Efficient Fine-Tuning (PEFT) methodologies have been proposed as computational alternatives; however, their implementations still require significant resources. In this paper, we present OSoRA (Output-Dimension and Singular-Value Initialized Low-Rank Adaptation), a novel PEFT method for LLMs. OSoRA extends Low-Rank Adaptation (LoRA) by integrating Singular Value Decomposition (SVD) with learnable scaling vectors in a unified framework. It first performs an SVD of pre-trained weight matrices, then optimizes an output-dimension vector during training, while keeping the corresponding singular vector matrices frozen. OSoRA substantially reduces computational resource requirements by minimizing the number of trainable parameters during fine-tuning. Comprehensive evaluations across mathematical reasoning, common sense reasoning, and other benchmarks demonstrate that OSoRA achieves comparable or superior performance to state-of-the-art methods like LoRA and VeRA, while maintaining a linear parameter scaling even as the rank increases to higher dimensions. Our ablation studies further confirm that jointly training both the singular values and the output-dimension vector is critical for optimal performance.

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