A research lab is training a series of language models, progressively increasing model size, dataset size, and computational budget. They observe that their models' performance improvements follow a predictable power-law relationship initially. However, after reaching a certain scale, they notice that further increases in model size and compute lead to significantly smaller gains in performance than predicted, eventually causing the performance to plateau. Analyze the potential underlying reasons for this deviation from the expected scaling behavior. In your analysis, consider factors related to the training data, the model architecture, and the training process itself.