The article explores how to learn approximate Nash equilibrium in bimatrix games. The researchers show that a certain type of function class can be effectively learned with respect to Nash approximation loss. They also develop a model that can efficiently approximate solutions for games under the same distribution. Experiments demonstrate that the solutions from their Nash predictor can be used as effective starting points for other Nash solvers.