Chinchilla Scaling Law Formula

A research team is training a large language model and observes that the model's performance, measured by test loss, seems to be primarily limited by the number of model parameters rather than the amount of training data. According to the principle that models test loss as a sum of two separate terms—one that decreases as model size grows and another that decreases as dataset size grows—which of the following actions would most effectively reduce the test loss in this specific situation?

Critique of a Model Training Strategy

Analyzing Performance Limits in Language Models