AdEMAMix: Enhancing Gradient Efficiency for Large-Scale Model Training
Practical Solutions and Value
Machine learning, especially deep learning, relies on optimization algorithms like Stochastic Gradient Descent (SGD) to train large-scale models for tasks such as language processing and image classification. However, traditional optimizers like Adam and AdamW may struggle to effectively use older gradient information, leading to suboptimal convergence rates and performance in large-scale training scenarios.
AdEMAMix introduces a novel approach by incorporating a dual-EMA system, balancing the need to respond to recent updates while retaining valuable older gradients often discarded by existing optimizers. This results in more efficient training of large-scale models, reducing the total number of tokens needed for training while achieving comparable or better results.
Performance evaluations have shown substantial improvements in speed and accuracy over existing optimizers, with AdEMAMix consistently outperforming AdamW in trials. The method’s ability to reduce model forgetting during training further underscores its value for large-scale, long-term ML projects, making it a powerful tool for researchers and industry.
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