Learning curve extrapolation aims to predict model performance in later epochs of a machine learning training, based on the performance in the first k epochs. In this work, we argue that, while the varying difficulty of extrapolating learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive. We describe the first application of prior-data fitted neural networks (PFNs) in this context. PFNs use a transformer, pre-trained on da…