Supplementary Material for " Neural Auto-Curricula in Two-player Zero-sum Games " Table of Contents 15 A.1 MLP-based Meta-Solver 15 A.2 Conv1D-based Meta-Solver

In this section, we recap the meta-solver properties that we need and illustrate how we designed models to achieve them. There exist two properties the model should have. The model should handle a variable-length matrix input. The model should be subject to row-permutation equivariance and column-permutation invariance. Three different techniques can be utilised to achieve the first property, which also corresponds to the three different models we propose: MLP based, Conv1d based and GRU based model. If not specifically mentioned, we utilise ReLU as the activation function for all MLP used in our meta-solver.