Enhancing the training effectiveness of Generative Flow Networks (GFlowNets) through local search to improve mode seeking and average reward performance.
Local Search GFlowNets enhances training effectiveness by leveraging local search in object space, improving mode seeking and average reward performance.
Dynamic Backtracking GFN (DB-GFN) enhances the adaptability of GFlowNet decision-making through a reward-based dynamic backtracking mechanism, enabling more efficient exploration of the sampling space and generating higher-quality samples.