FP is used as an input to a stack of three feed-forward layers, with hidden dimensions of [256,128,1], a GeLU activation function after the first and second layers, and the last linear layer mapping to one output in the simplest model. Several small variations of the FP predictor stack were explored, depending on the formulation of the learning task as described next.