There's no slowing down the godfathers of deep learning, who continue to innovate. Several years ago Geoffrey Hinton introduced Capsule Networks (CapsNets) for dynamic image modelling, and this past summer a Yoshua Bengio team proposed Generative Flow Networks (GFlowNets), a low-network-based generative method that can turn a given positive reward into a generative policy that samples with a probability proportional to the return. GFlowNets achieve competitive results on molecule synthesis domain tasks and perform well on a simple domain where there are many modes to the reward function. In the new paper GFlowNet Foundations, a research team from Mila, University of Montreal, McGill University, Stanford University, CIFAR and Microsoft Azure AI builds upon GFlowNets, providing an in-depth formal foundation and expansion of the set of theoretical results for a broad range of scenarios, especially active learning. GFlowNets are inspired by the way information propagates in temporal-difference reinforcement learning.