Dynamic Gradient Scaling: A Fine-Grained Approach to Optimizing Large Language Models in Deep Learning

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. However, the training of such models is often hindered by optimization challenges, leading to inefficiencies and suboptimal performance. In this paper, I propose Dynamic Gradient Scaling (DGS), a novel fine-grained optimization technique tailored for the unique demands of LLMs. DGS dynamically adjusts learning rates based on the importance of individual parameters, allowing for enhanced efficiency and control during the optimization process. Theoretical foundations of DGS are explored, elucidating the calculation of importance scores, scaling factors, and adaptive learning rates. Practical implementation within the training loop of deep learning models showcases the versatility of DGS across diverse tasks. My research demonstrates that DGS provides fine-grained control over the optimization process, yielding improvements in training efficiency and model performance.