Investigating Generalization Behaviours of Generative Flow Networks

2024-02-07

arXiv.org

AI Summary

This study systematically investigates the generalization mechanisms of Generative Flow Networks (GFlowNets) in discrete spaces. To rigorously test generalization hypotheses, we introduce a graph-structured benchmark environment with tunable reward difficulty, enabling exact computation of the true distribution $p(x)$ and precise quantification of generalization error. Methodologically, we establish a unified training–evaluation framework to conduct offline and off-policy generalization analysis, as well as experiments on implicit reward robustness. Our key contributions are threefold: (1) We empirically demonstrate that GFlowNet generalization arises from implicit structural priors encoded in the learned flow function—not from explicit regularization; (2) We reveal high sensitivity to training distribution shift, yet strong robustness to perturbations in implicit reward signals; (3) We challenge the prevailing assumption that GFlowNets inherently generalize better than alternatives, providing new empirical foundations and design insights for generalization theory in discrete generative modeling.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favorable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. We accomplish this by introducing a novel graph-based benchmark environment where reward difficulty can be easily varied, $p(x)$ can be computed exactly, and an unseen test set can be constructed to quantify generalization performance. Using this graph-based environment, we are able to systematically test the hypothesized mechanisms of generalization of GFlowNets and put forth a set of empirical observations that summarize our findings. In particular, we find (and confirm) that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. Surprisingly -- and somewhat contradictory to existing knowledge -- we also find that GFlowNets are sensitive to being trained offline and off-policy. However, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.

Problem

• Investigates generalization behaviors of Generative Flow Networks (GFlowNets).
• Tests hypothesized mechanisms of GFlowNets' generalization using a graph-based benchmark.
• Explores GFlowNets' sensitivity to offline and off-policy training.

Innovation

• GFlowNets paired with DNNs for generalization
• Novel graph-based benchmark for reward variation
• Implicit structure in GFlowNets aids generalization