We propose a new objective for generative adversarial networks (GANs) that is aimed to address current issues in GANs such as mode collapse and unstable convergence. Our approach stems from the hockey-stick divergence that has properties we claim to be of great importance in generative models. We provide theoretical support for the model and preliminary results on synthetic Gaussian data.