Scale has become a main ingredient in obtaining strong machine learning
models. As a result, understanding a model's scaling properties is key to
effectively designing both the right training setup as well as future
generations of architectures. In this work, we argue that scale and training
research has been needlessly complex due to reliance on the cosine schedule,
which prevents training across different lengths for the same model size. We
investigate the training behavior of a direct alternative - constant learning
rate and cooldowns - and find that it scales predictably and reliably similar
to cosine. Additionally, we show that stochastic weight averaging yields
improved performance along the training trajectory, without additional training
costs, across different scales. Importantly, with these findings we demonstrate
that scaling experiments can be performed with significantly reduced compute
and GPU hours by utilizing fewer but reusable training runs. Our code is
available at this https URL

通过研究模型的规模和训练行为，本研究提出了常数学习率和冷却方法作为替代余弦调度的更简单且可预测可靠的训练方法，并发现随机权重平均可以在不增加额外训练成本的情况下改善训练过程中的性能，从而减少计算和 GPU 时间，实现规模实验的效率提升。