amortized-inference

Proximal Policy Optimization Achieves Faster Convergence in Discrete Sampling Research

A new paper on arXiv explores policy gradient algorithms for training stochastic policies under the Generative Flow Network (GFlowNet) framework. The authors derive equivalents of standard policy gradient algorithms and, for the first time, successfully apply proximal policy optimization (PPO) to GFlowNets, demonstrating improved convergence speed and data efficiency on benchmarks including synthetic energies and molecular graph generation.