Breaking Down Low-Rank Adaptation and Its Next Evolution, ReLoRA

Low-Rank Adaptation (LoRA) and its successor ReLoRA offer more efficient ways to fine-tune large AI models by reducing the computational and memory costs of traditional full-rank training. ReLoRA* extends this idea through zero-initialized layers and optimizer resets for even leaner adaptation—but its reliance on random initialization and limited singular value learning can cause slower convergence. The section sets the stage for Sparse Spectral Training (SST), which aims to resolve these bottlenecks and match full-rank performance with far lower resource demands.