Our objective is learning a policy that samples latent codes with probability proportional to their tempered reward π η (z|τ ) ∝ R τ (z). This approach bears similarity to recent work on Boltzmann generators and generative flow networks . The policy together with the decoder p θ (x|z, D) induces a distribution over X D :