Scalable Training of Diffusion Samplers via Fixed-point Score Matching

Denis Blessing presents scalable training of diffusion samplers via fixed point score matching: a unifying framework for learning Boltzmann-type distributions from energy evaluations alone, with applications to amorphous materials, alloy phase diagrams, and reinforcement learning with flow policies.