weight-factorization

This paper proposes Native Factorized Weights for transformers, where every linear layer is trained as a product of two low-rank matrices from initialization. Experiments show a corpus-determined optimal rank that minimizes validation loss and a generalization band, outperforming dense baselines with fewer parameters.