Less is More: Resource-Efficient Low-Rank Adaptation

ACL ARR 2026 January Submission5528 Authors

Keywords: Large Language Models; Efficient Fine-Tuning; Low-rank Adaptation;

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. \model~ consistently outperforms LoRA across diverse modalities, including commonsense reasoning, visual instruction tuning, and image generation, demonstrating improved efficiency and robustness. Anonymous codes are submitted with the paper and will be publicly available.

Paper Type: Long

Research Area: LLM Efficiency

Research Area Keywords: LLM Efficiency; NLP in resource-constrained settings;

Contribution Types: Approaches to low-resource settings

Languages Studied: English

Submission Number: 5528