Scholarlink

Lion is a recently introduced and highly relevant method for optimizing neural networks. Research has shown that it can outperform the widely used AdamW optimizer for certain tasks. However, the performance of any optimization algorithm is significantly influenced by its hyperparameters. This article explores the problem of selecting hyperparameters for the Lion optimizer through comparative analysis.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Lion Optimizer: the Impact of Hyperparameter Selection on Model Training Quality

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Dmitrii A. Mikhailov,
  • Maxim V. Abramov

摘要

Lion is a recently introduced and highly relevant method for optimizing neural networks. Research has shown that it can outperform the widely used AdamW optimizer for certain tasks. However, the performance of any optimization algorithm is significantly influenced by its hyperparameters. This article explores the problem of selecting hyperparameters for the Lion optimizer through comparative analysis.