Global-Order GFlowNets

• URL: http://arxiv.org/abs/2504.02968v1
• Date: Thu, 03 Apr 2025 18:43:52 GMT
• Title: Global-Order GFlowNets
• Authors: Lluís Pastor-Pérez, Javier Alonso-Garcia, Lukas Mauch,
• Abstract summary: Order-Preserving (OP) GFlowNets have demonstrated remarkable success in tackling complex multi-objective (MOO) black-box optimization problems.<n>We introduce Global-Order GFlowNets, which transform the local order into a global one, thereby resolving these conflicts.
• Score: 0.36681882674260474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Order-Preserving (OP) GFlowNets have demonstrated remarkable success in tackling complex multi-objective (MOO) black-box optimization problems using stochastic optimization techniques. Specifically, they can be trained online to efficiently sample diverse candidates near the Pareto front. A key advantage of OP GFlowNets is their ability to impose a local order on training samples based on Pareto dominance, eliminating the need for scalarization - a common requirement in other approaches like Preference-Conditional GFlowNets. However, we identify an important limitation of OP GFlowNets: imposing a local order on training samples can lead to conflicting optimization objectives. To address this issue, we introduce Global-Order GFlowNets, which transform the local order into a global one, thereby resolving these conflicts. Our experimental evaluations on various benchmarks demonstrate the efficacy and promise of our proposed method.

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