AI RESEARCH
SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
arXiv CS.LG
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ArXi:2605.20619v1 Announce Type: new Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solutions that represent diverse user preferences, ideally with uniform coverage of the Pareto front (PF). However, uniformly sampling scalarization weights usually induces non-uniform coverage of the PF. We explain this mismatch through a geometric analysis of the scalarization path. As the scalarization weight varies, the corresponding solutions trace the PF with a generally non-uniform traversal speed.