Lattice Configuration Generation with a Self-Learning Diffusion Model

We show that a diffusion sampler for lattice-field configurations can be self-trained without preparing target-ensemble training configurations using an external Monte Carlo calculation. Starting from exactly sampled configurations at $β=0$, we use action-difference weights to train the score at the