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We introduce Goldilocks Selection, a technique for faster model trainingwhich selects a sequence of training points that are "just right". We proposean information-theoretic acquisition function -- the reducible validation loss-- and compute it with a small proxy model -- GoldiProx -- to efficientlychoose training points that maximize information about a validation set. Weshow that the "hard" (e.g. high loss) points usually selected in theoptimization literature are typically noisy, while the "easy" (e.g. low noise)samples often prioritized for curriculum learning confer less information.Further, points with uncertain labels, typically targeted by active learning,tend to be less relevant to the task. In contrast, Goldilocks Selection choosespoints that are "just right" and empirically outperforms the above approaches.Moreover, the selected sequence can transfer to other architectures;practitioners can share and reuse it without the need to recreate it.

Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)