arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during pretraining. As a result, noisy gradients from limited fine-tuning data can perturb robust pretrained features. We identify spectral preconditioning as the missing ingredient: reparameterizing each weight matrix through its full-rank singular value decomposition (SVD) and freezing one singular basis constrains updates to the pretrained column space, yielding a preconditi
The paper addresses current challenges in fine-tuning large AI models, particularly the issue of noisy gradients and perturbing pretrained features with limited data, a common problem in real-world AI applications.
This development offers a more efficient and robust method for fine-tuning AI models, potentially leading to faster development cycles, reduced computational costs, and more stable performance in deployed AI systems.
The proposed 'FuRA' method changes how weight updates are applied during fine-tuning, preserving the spectral structure of pretrained models and making fine-tuning more resilient to noise.
- · AI developers
- · Cloud computing providers
- · Companies deploying AI
- · Researchers in machine learning
- · Inefficient fine-tuning methods
- · Companies with high compute costs for model adaptation
Improved performance and stability of AI models with less data and compute.
Accelerated deployment of specialized AI applications across various industries due to easier fine-tuning.
Potentially democratizes access to advanced AI capabilities by lowering resource barriers for model adaptation.
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Read at arXiv cs.LG