Data Mixing

Data mixing, the technique of combining diverse datasets for training machine learning models, aims to improve model generalization and efficiency. Current research focuses on optimizing data mixture proportions, often employing gradient alignment algorithms or bivariate scaling laws to predict optimal combinations and reduce computational costs. These advancements are particularly relevant for large language models and self-supervised learning, enhancing performance on downstream tasks and improving data efficiency in resource-constrained environments. The resulting improvements in model accuracy and robustness have significant implications for various applications, including satellite navigation and natural language processing.