Reinforce Adjoint Matching: Scaling Diffusion RL

Andreas Bergmeister presents Reinforce Adjoint Matching (RAM), a novel method for scalable reinforcement learning post-training of diffusion models. RAM sidesteps the complexity of existing RL approaches by deriving a consistency loss that directly corrects the pre-training target with a reward signal, eliminating the need for costly SDE rollouts or reward gradients. This approach achieves superior performance and up to 50x faster convergence on challenging image generation tasks like complex composability, text rendering, and human preference alignment.