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Mike Young
Mike Young

Posted on • Originally published at aimodels.fyi

Simple SGD Method Matches Adam's Performance While Using Half the Memory

This is a Plain English Papers summary of a research paper called Simple SGD Method Matches Adam's Performance While Using Half the Memory. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Overview

  • SGD-SaI enhances classic stochastic gradient descent with momentum
  • Adjusts learning rates at initialization based on gradient signal-to-noise ratios
  • Uses half the memory of AdamW while matching or exceeding performance
  • Effective for training Transformers, Vision Transformers, and large language models
  • Reduces memory usage by up to 25GB for large models like Llama2-7B

Plain English Explanation

Think of training an AI model like teaching a student. Traditional methods (like AdamW) are like having a separate tutor for each concept, requiring lots of resources. SGD-SaI is more like having one rea...

Click here to read the full summary of this paper

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