Hyperparameter
A configuration value set before training (e.g. learning rate, batch size) rather than learned by the model.
Hyperparameters govern how training proceeds. Choosing them well — often via sweeps or smaller proxy runs — has a large effect on the final model, and is distinct from the parameters (weights) the model learns.