The content delves into the analysis of optimization trajectories in neural networks and large language models. It discusses the impact of hyperparameters like momentum, weight decay, batch size, and learning rate on directional exploration. The study provides insights into how these factors affect the structure of optimization paths and generalization strategies.
The authors propose a fresh perspective on understanding neural networks by analyzing their optimization trajectories. They introduce qualitative and quantitative indicators to reveal the complexity of these trajectories. Experiments are conducted over large-scale vision and language settings to demonstrate the value of their approach.
Key findings include observations about momentum, weight decay, batch size, learning rate, and scale affecting directional exploration in optimization paths. The study also suggests potential implications for faster training algorithms based on trajectory redundancy.
Overall, the research aims to deepen understanding of optimization behaviors in deep learning systems through a comprehensive analysis of optimization trajectories.
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