Pretraining progress tracks how far a model has advanced through its initial learning phase on large datasets. Teams use it to gauge convergence, tune compute budgets, and decide when to fine-tune. ML engineers, researchers, and product leads benefit, gaining early signals on model quality before costly downstream training.
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