FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

This research introduces FuRA (Full-Rank Adaptation), a novel parameter-efficient fine-tuning method that addresses limitations in existing techniques by incorporating spectral preconditioning. By reparameterizing weight matrices via full-rank Singular Value Decomposition and constraining updates, FuRA outperforms unconstrained Full Fine-Tuning while maintaining efficiency.