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Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
[Submitted on 25 Feb 2026 (v1), last revised 28 May 2026 (this v · 2026-05-29 · via cs.LG updates on arXiv.org

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Abstract:Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has attracted growing interest as real-world applications often involve multiple, conflicting objectives. However, existing approaches require joint training for each combination of objectives, meaning that any change in the objective set necessitates retraining from scratch. We propose a framework that composes pre-trained GFlowNets at inference time, enabling rapid adaptation without fine-tuning or retraining. Importantly, our framework is flexible, capable of handling diverse reward combinations ranging from linear scalarization to complex nonlinear operators, which are often handled separately in previous literature. We prove that our method exactly recovers the target distribution for linear scalarization, and quantify the approximation quality for nonlinear operators through a distortion factor. Experiments on a synthetic 2D grid and real-world molecule generation tasks demonstrate that our approach achieves performance comparable to baselines.

Submission history

From: Seokwon Yoon [view email]
[v1] Wed, 25 Feb 2026 04:44:46 UTC (2,005 KB)
[v2] Thu, 28 May 2026 12:23:04 UTC (2,249 KB)

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