  Scaling of deep neural networks, especially Transformers, is pivotal for
their surging performance and has further led to the emergence of sophisticated
reasoning capabilities in foundation models. Such scaling generally requires
training large models from scratch with random initialization, failing to
leverage the knowledge acquired by their smaller counterparts, which are
already resource-intensive to obtain. To tackle this inefficiency, we present
$\textbf{L}$ossl$\textbf{E}$ss $\textbf{MO}$del Expansio$\textbf{N}$ (LEMON), a
recipe to initialize scaled models using the weights of their smaller but
pre-trained counterparts. This is followed by model training with an optimized
learning rate scheduler tailored explicitly for the scaled models,
substantially reducing the training time compared to training from scratch.
Notably, LEMON is versatile, ensuring compatibility with various network
structures, including models like Vision Transformers and BERT. Our empirical
results demonstrate that LEMON reduces computational costs by 56.7% for Vision
Transformers and 33.2% for BERT when compared to training from scratch.

Machine Learning;Machine Learning