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

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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...

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