Why Sparse Spectral Training Might Replace LoRA in AI Model Optimization

Sparse Spectral Training (SST) is a novel method for optimizing neural networks by selectively updating significant spectral components rather than all weight parameters. By refining singular values (Σ) and decoupling magnitude from direction during training, SST achieves near full-rank performance with lower computational overhead. It avoids saddle point stagnation common in zero-initialized LoRA methods, balancing exploration and exploitation for more stable convergence. The result: faster, memory-efficient AI models that maintain accuracy without the cost of dense updates.