AI RESEARCH
Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv CS.LG
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ArXi:2602.21565v2 Announce Type: replace 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