Generative Flow Network

Generative Flow Networks (GFlowNets) are a class of generative models designed to sample complex objects from unnormalized probability distributions, often proportional to a reward function, by framing the sampling process as a sequential decision-making problem. Current research focuses on improving training efficiency and exploration strategies, particularly through novel loss functions, meta-learning techniques, and integration with methods like Monte Carlo Tree Search and reinforcement learning, including exploration of both discrete and continuous state spaces and multi-agent scenarios. GFlowNets offer a powerful alternative to traditional methods for generating diverse, high-quality samples in various domains, impacting fields like drug discovery, materials science, and combinatorial optimization by enabling efficient exploration of vast search spaces.