UED is reframed as an entropy-regularized minimax problem with two-timescale gradient convergence guarantees for zero-sum scores, and a generalized learnability score improves robustness on three benchmarks, with the best empirical variants lying outside the guaranteed regime.
Accelerated algorithms for constrained nonconvex-nonconcave min-max optimization and comonotone inclusion
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An Optimisation Framework for Unsupervised Environment Design
UED is reframed as an entropy-regularized minimax problem with two-timescale gradient convergence guarantees for zero-sum scores, and a generalized learnability score improves robustness on three benchmarks, with the best empirical variants lying outside the guaranteed regime.