A machine learning team is training a series of language models. They systematically increase the size of the training dataset for each new model and record the final test loss. When they plot the test loss versus the dataset size on a graph where both axes use a logarithmic scale, they observe the points form a nearly straight, downward-sloping line. What is the most valid interpretation of this trend?

Three Phases of LLM Scaling with Dataset Size

Strategic Model Improvement

Interpreting Training Anomalies

Empirical Power Law for LLM Loss vs. Dataset Size (D)