Summary of Grams: Gradient Descent with Adaptive Momentum Scaling, by Yang Cao et al.
Grams: Gradient Descent with Adaptive Momentum Scaling
by Yang Cao, Xiaoyu Li, Zhao Song
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel optimization algorithm, Gradient Descent with Adaptive Momentum Scaling (Grams), is introduced for deep learning. Unlike traditional optimizers, Grams decouples the direction and magnitude of parameter updates by separating the update direction from momentum used solely for adaptive scaling. This approach enables improved loss descent compared to state-of-the-art cautious and momentum-based optimizers. Theoretical demonstrations show that Grams descends faster than other optimizers, and a global convergence guarantee is established. Empirical evaluations validate Grams’ effectiveness, demonstrating superior performance in terms of convergence speed and generalization compared to Adam, Lion, and their cautious variants. This paper highlights Grams’ potential as a transformative approach for efficiently training and fine-tuning large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Grams is a new way to train deep learning models. It helps the model learn faster and make better predictions by adjusting how it updates its parameters. Unlike other methods, Grams separates two important parts: where the model is moving (the direction) and how fast it’s moving (the magnitude). This lets Grams learn more efficiently and make better predictions than other popular methods like Adam and Lion. The results show that Grams can train models faster and with better results. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Generalization » Gradient descent » Optimization
