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October 8, 2025Open Access

Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning

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Proposed Localized LoRA enables efficient weight updates in models, suggesting improved performance and flexibility.

Localized LoRA consistently achieves lower approximation error compared to existing methods, enhancing model efficiency.
Formal comparisons illustrate the advantages of localized updates over global low rank structures, pointing to increased performance.
The proposed method avoids adding trainable parameters while enabling more expressive weight modifications, contributing to effective fine-tuning.
Experiments confirm the framework's adaptability across synthetic and practical settings, indicating significant potential for broader applications.

Barazandeh et al. (2025) studied this question.

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