Concept Drift

Compute-Optimal Models are designed to maximize performance within a specific computational budget by balancing model size and training data. Using scaling laws, such as Chinchilla, these models typically prioritize training on larger datasets rather than increasing parameters, resulting in more efficient and effective models that achieve better accuracy and generalization while respecting computational constraints.