DART: Distributional Adversarial Recurrent Training for Algorithm Learning
Distributional Adversarial Recurrent Training (DART), a training framework that replaces single-point supervision with a local target distribution around the ground-truth solution and aligns model outputs with this distribution through an adversarial objective, is proposed.