2024/07/03 by Anas Krichel, Krichel, Anas, Nikolay Malkin +5 · 1 citation
Computer Science · Decision Sciences · Engineering · #Advanced Database Systems and Queries #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scheduling and Optimization Algorithms #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2407.03105
openalex publication_date 2024/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Generative Flow Networks (GFlowNets) have emerged as an innovative learning paradigm designed to address the challenge of sampling from an unnormalized probability distribution, called the reward function. This framework learns a policy on a constructed graph, which enables sampling from an approximation of the target probability distribution through successive steps of sampling from the learned policy. To achieve this, GFlowNets can be trained with various objectives, each of which can lead to the model s ultimate goal. The aspirational strength of GFlowNets lies in their potential to discern intricate patterns within the reward function and their capacity to generalize effectively to novel, unseen parts of the reward function. This paper attempts to formalize generalization in the context of GFlowNets, to link generalization with stability, and also to design experiments that assess the capacity of these models to uncover unseen parts of the reward function. The experiments will focus on length generalization meaning generalization to states that can be constructed only by longer trajectories than those seen in training.