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SDS-LoRA: New Low-Rank Adaptation Method Fixes Gradient Distortion in Large Model Fine-Tuning

Artificial Intelligence #machine learning#low-rank adaptation

SDS-LoRA: New Low-Rank Adaptation Method Fixes Gradient Distortion in Large Model Fine-Tuning

A new paper on arXiv introduces SDS-LoRA, a low-rank parameterization that overcomes anisotropic gradient scaling in LoRA. By structurally decoupling singular values from the backward pass, SDS-LoRA ensures gradients are only applied through orthonormal bases, improving convergence and reducing the performance gap to full fine-tuning. Experimental results across natural language and vision benchmarks show enhanced adaptation performance.

Jun 16, 2026 1 source