Papers › Grams: Gradient Descent with Adaptive Momentum Scaling
Grams: Gradient Descent with Adaptive Momentum Scaling
Yang Cao, Xiaoyu Li, Zhao Song
We introduce 𝐆radient Descent with 𝐀daptive 𝐌omentum 𝐒caling (𝐆𝐫𝐚𝐦𝐬), a novel optimization algorithm that decouples the direction and magnitude of parameter updates in deep learning. Unlike traditional optimizers that directly integrate momentum into updates, Grams separates the update direction, derived from current gradients, from momentum, which is used solely for adaptive magnitude scaling. This approach enables Grams to achieve improved loss descent compared to state-of-the-art cautious and momentum-based optimizers. We theoretically demonstrate that Grams descents faster than other state-of-the-art optimizers and establish a global convergence guarantee for Grams. We also validate its effectiveness through extensive empirical evaluations. The results demonstrate Grams' superior performance, including faster convergence and better generalization, compared to widely-used optimizers such as Adam, Lion, and their cautious variants. Our results highlight Grams' potential as a transformative approach for efficiently training and fine-tuning large language models. Code is available at https://github.com/Gunale0926/Grams.
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