Less is More: Resource-Efficient Low-Rank Adaptation

Chunlin Tian; Xuyang Wei; Huanrong Liu; Zhijiang Guo; Li Li

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arXiv · 2512.00878

Less is More: Resource-Efficient Low-Rank Adaptation

Chunlin Tian·Xuyang Wei·Huanrong Liu·Zhijiang Guo·Li Li

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Abstract

Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method for Large Language Models (LLMs), but it still incurs notable overhead and suffers from parameter interference in complex datasets. While recent works decouple LoRA update matrices to exploit matrix-wise asymmetry, training costs remain high. We revisit LoRA from the perspective of inter-matrix and intra-layer parameter redundancy and propose Resource-Efficient Low-Rank Adaptation, EffiLoRA, a lightweight and generalizable approach for language, multimodal, and diffusion models. EffiLoRA employs a unified A matrix across all transformer layers and introduces a runtime selective B matrices update to dynamically trade-off the system resource budget and model performance. EffiLoRA consistently outperforms LoRA across diverse modalities, including commonsense reasoning, visual instruction tuning, and image generation, demonstrating improved efficiency and robustness.

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BibTeX RIS

Chunlin Tian, Xuyang Wei, Huanrong Liu, Zhijiang Guo, Li Li. 2025-11-30. Less is More: Resource-Efficient Low-Rank Adaptation. https://arxiv.org/abs/2512.00878

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