Less is More: Resource-Efficient Low-Rank Adaptation

• URL: http://arxiv.org/abs/2512.00878v1
• Date: Sun, 30 Nov 2025 12:52:04 GMT
• Title: Less is More: Resource-Efficient Low-Rank Adaptation
• Authors: Chunlin Tian, Xuyang Wei, Huanrong Liu, Zhijiang Guo, Li Li,
• Abstract summary: EffiLoRA is a lightweight and generalizable approach for language, multimodal, and diffusion models.<n>It consistently outperforms LoRA across diverse modalities, including commonsense reasoning, visual instruction tuning, and image generation.
• Score: 15.883867662707743
• License: http://creativecommons.org/licenses/by/4.0/
• 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 re- cent 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 up- date 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, demon- strating improved efficiency and robustness.

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