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[2505.18877] RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Summary
The paper presents RefLoRA, a novel method for fine-tuning large models by optimizing low-rank adaptations, leading to faster convergence and improved performance over existing techniques.
Why It Matters
RefLoRA addresses the limitations of traditional Low-Rank Adaptation (LoRA) methods, which can suffer from slow convergence and performance degradation. By promoting a flatter loss landscape and balanced weight updates, it enhances the efficiency of fine-tuning large models, which is crucial for advancing applications in natural language processing and machine learning.
Key Takeaways
- RefLoRA optimizes low-rank adaptations for better fine-tuning of large models.
- The method achieves faster convergence and improved performance compared to existing LoRA variants.
- Extensive experiments validate RefLoRA's effectiveness on various natural language understanding tasks.
- RefLoRA maintains negligible computational overhead while enhancing model training efficiency.
- The approach is particularly relevant for applications in AI and machine learning.
Computer Science > Machine Learning arXiv:2505.18877 (cs) [Submitted on 24 May 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Authors:Yilang Zhang, Bingcong Li, Georgios B. Giannakis View a PDF of the paper titled RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models, by Yilang Zhang and 2 other authors View PDF HTML (experimental) Abstract:Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss landscape, along with consistent and balanced weight updates, thus speeding up stable convergence. Extensive experiments evaluate RefLoRA on natural language understanding, and commonsense reasoning tasks with popular large language models including DeBERTaV3, LLaMA-7B, LLaMA2-7B and LLaMA3-8B. The numerical tests corroborate that RefLoRA converges faster, outperforms various benchmarks, and enjoys negligible computational overhe...
