发现学习率与数据集大小的隐藏比例关系，可优化训练效率。
Tuning Learning Rates with the Cumulative-Learning Constant
- 通过分析学习率与数据集规模的关联性，提出新调参思路。
- 识别出累积学习常数，为学习率调度提供理论框架。
- 适合追求高效训练的模型开发者和研究者。
本文提出一种优化机器学习中学习率的新方法。研究发现学习率与数据集规模之间存在此前未被识别的比例关系，揭示了数据规模对训练动态的影响机制。同时，提出一个累积学习常数概念，为设计和优化先进学习率调度策略提供了理论框架。这些发现有望在广泛机器学习应用中提升训练效率与性能。
原文摘要 · Abstract (English)
This paper introduces a novel method for optimizing learning rates in machine learning. A previously unrecognized proportionality between learning rates and dataset sizes is discovered, providing valuable insights into how dataset scale influences training dynamics. Additionally, a cumulative learning constant is identified, offering a framework for designing and optimizing advanced learning rate schedules. These findings have the potential to enhance training efficiency and performance across a wide range of machine learning applications.
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