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Alternating learning dynamics achieve O(log d / T) convergence to Nash equilibria in two-player zero-sum games and O(log d / T) convergence to coarse correlated equilibria in two-player general-sum games

As a result, we obtain alternating learning dynamics with $O(\log d /T)$ convergence to Nash equilibria in two-player zero-sum games and $O(\log d /T)$ convergence to coarse correlated equilibria in two-player general-sum games.
Machine Learning (Statistics)28 Aug 2026

http://arxiv.org/abs/2608.24731v1