  We derive the rate of convergence to the strongly variationally stable Nash
equilibrium in a convex game, for a zeroth-order learning algorithm. Though we
do not assume strong monotonicity of the game, our rates for the one-point
feedback and for the two-point feedback match the best known rates for strongly
monotone games under zeroth-order information.
