Alexandru Meterez, Pranav Ajit Nair, Depen Morwani, Cengiz Pehlevan, Sham Kakade
This paper introduces anytime pretraining schedules using weight averaging, which provide effective learning rate strategies for language models without needing a fixed training horizon.
In the world of artificial intelligence, training large language models is a complex task often requiring precise planning and tuning of learning rates based on a predetermined training duration. However, this research explores a new approach that doesn't rely on knowing how long the training will last. The study highlights the importance of weight averaging—a technique where the model parameters are averaged over time—to achieve efficient learning. The findings show that these new, flexible learning schedules can perform just as well as traditional methods, offering a simpler and effective way to train models without the need for a fixed timeline.
