Coarse-to-fine tuning is a hyperparameter search strategy that starts by sampling hyperparameter values spread widely across the search space. After training and evaluating models at these coarse points, the search zooms into the smaller region(s) where performance was best and samples more densely within that region to refine the values. This zoom-in step can be repeated, progressively narrowing the search until the most optimal hyperparameter values are found.