Goal Conditioned GFlowNets

Goal-conditioned Generative Flow Networks (GFlowNets) are a class of generative models designed to efficiently sample high-reward objects from complex spaces, addressing limitations of traditional reinforcement learning methods. Current research focuses on improving GFlowNet training efficiency, particularly in scenarios with sparse rewards, through techniques like retrospective backward synthesis and pre-training/fine-tuning strategies. These advancements enable applications in diverse fields such as molecular design and combinatorial optimization, where generating diverse, high-quality solutions is crucial, by allowing for controllable exploration of the solution space and handling multiple, potentially conflicting objectives. The resulting ability to efficiently sample from complex, unnormalized distributions holds significant promise for accelerating scientific discovery and optimization across various domains.