This paper explores improving how AI agents coordinate with humans in cooperative tasks by addressing the challenge of training agents on the vast diversity of human behaviors. The authors introduce a new method called GOAT (Generative Online Adversarial Training), which combines a pre-trained generative model of cooperative strategies with adversarial training. This framework uses an Adversary agent to find challenging but realistic human-like partners (simulated by the generative model) that expose the learning Cooperator agent's weaknesses. By optimizing a regret-based objective, GOAT encourages the Cooperator to learn robust coordination skills and achieve state-of-the-art performance when collaborating with novel human partners in the Overcooked benchmark.