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今天的主题是：The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

Summary

This paper explores the surprising parallels between learning-rate schedules used in large model training and theoretical performance bounds from convex optimization. It demonstrates that a simple learning-rate schedule with a constant phase followed by a linear cooldown mirrors the behavior predicted by theory, even for non-convex deep learning problems. Furthermore, the research shows how this theoretical understanding can be practically applied to improve learning-rate tuning for continued training and transfer optimal rates across different schedules, leading to tangible gains in model performance. The work provides theoretical justification for empirically successful scheduling techniques and suggests that principles from convex optimization offer valuable insights into the training of complex neural networks.

本文探讨了大型模型训练中使用的学习率调度与凸优化理论性能界限之间的惊人相似性。研究表明，一个简单的学习率调度方案——先保持恒定，然后线性降温——即使在非凸深度学习问题中，也能呈现出与理论预测相符的行为。此外，研究还展示了如何将这一理论理解实际应用于改进持续训练的学习率调优，并在不同的调度方案之间转移最优学习率，从而显著提升模型性能。本研究为经验上成功的调度技术提供了理论依据，并表明凸优化的原理可以为复杂神经网络的训练提供有价值的见解。

原文链接：https://arxiv.org/abs/2501.18965