GFlowNet Training

GFlowNet training focuses on efficiently learning stochastic policies to generate combinatorial objects with desired properties, framed as a sequential decision-making problem. Current research emphasizes improving training efficiency and stability through techniques like policy gradient methods, Monte Carlo Tree Search integration, and evolutionary algorithms, often addressing challenges posed by long sequences and sparse rewards. These advancements aim to enhance the performance and applicability of GFlowNets across diverse domains, particularly in scientific discovery and design where generating high-reward objects is crucial. Ongoing work also explores pre-training strategies and novel loss functions to further optimize the learning process and improve generalization.