OFA's factorized embedding parameterization contributes to efficiency in large-scale multilingual models by reducing the number of trainable parameters. By decomposing the embeddings into lower-dimensional embeddings and a primitive basis, OFA effectively reduces the computational burden during training. This reduction in parameters not only speeds up convergence but also allows for more efficient memory usage and faster adaptation to new languages during continued pretraining. Additionally, leveraging external multilingual word vectors to initialize subword embeddings further enhances efficiency by injecting semantic similarity knowledge into the model.