How loss and capability change with parameters, data and compute, and how labs plan training runs from them.
How benchmark scores of language models are produced and what can go wrong: prompt format, contamination, judge models, metric choice and predicting task scores from loss.
Empirical power laws relating loss to model size, data and compute, used to choose how big a model to train and on how many tokens before spending the budget.