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The aim is to develop LoLDU, a novel fine-tuning method that minimizes parameters while preserving model accuracy.

February 9, 2026

LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning.

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The aim is to develop LoLDU, a novel fine-tuning method that minimizes parameters while preserving model accuracy.
Proposed low-rank LDU approach to initialize matrices for faster convergence.
Compared LoLDU against traditional PEFT methods on multiple datasets and model types.
Optimized the diagonal matrix specifically for scaling transformations.
Achieved a reduction of trainable parameters by 2600 times compared to regular PEFT methods.
Maintained performance comparable to full fine-tuning despite fewer parameters.
Demonstrated across various datasets including instruction-following, natural language understanding, and image classification.

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LoLDU reduces trainable parameters for fine-tuning models, indicating improved efficiency and performance.

Shi et al. (2026) studied this question.

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