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This is a Experimental Study study.

September 29, 2025Open Access

1LoRA: Summation Compression for Very Low-Rank Adaptation

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Our analysis demonstrates that 1LoRA improves fine-tuning efficiency in large models, suggesting advancements in low-rank adaptation.

1LoRA achieves superior performance while being more parameter-efficient than existing methods.
This method fine-tunes fewer parameters per layer, enhancing memory and compute efficiency.
Extensive evaluations demonstrate that 1LoRA outperforms similar techniques like LoRA and VeRA.
By optimizing memory use, 1LoRA ensures more even fine-tuning across all layers of the model.

Quercia et al. (2025) studied this question.

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