Why Adam Beats SGD: New Study Reveals How Transformer Layer Differences Impact Training Success

This is a Plain English Papers summary of a research paper called Why Adam Beats SGD: New Study Reveals How Transformer Layer Differences Impact Training Success. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Overview

• Research examines why Adam optimizer performs better than SGD for transformer models
• Focuses on gradient heterogeneity in different transformer model layers
• Investigates relationship between optimization algorithms and model architecture
• Analyzes impact on training dynamics and model performance
• Provides empirical evidence through extensive experiments

Plain English Explanation

The research tackles a fundamental question in machine learning: why does the Adam optimizer work better than simpler methods when training large language models? Think of optimiz...

Click here to read the full summary of this paper