  We present energy-based generative flow networks (EB-GFN), a novel
probabilistic modeling algorithm for high-dimensional discrete data. Building
upon the theory of generative flow networks (GFlowNets), we model the
generation process by a stochastic data construction policy and thus amortize
expensive MCMC exploration into a fixed number of actions sampled from a
GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs
sampling to mix between modes. We propose a framework to jointly train a
GFlowNet with an energy function, so that the GFlowNet learns to sample from
the energy distribution, while the energy learns with an approximate MLE
objective with negative samples from the GFlowNet. We demonstrate EB-GFN's
effectiveness on various probabilistic modeling tasks. Code is publicly
available at https://github.com/zdhNarsil/EB_GFN.
