By treating forward-policy training as a geometric optimization problem, you can leverage the target distribution's structure (factorization, locality) to compute better gradient updates, leading to faster convergence and better exploration in GFlowNets.
This paper reformulates how to train the forward policy in GFlowNets (a framework for sampling from complex distributions) using information geometry.