Author

Arthur Corrêa

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Preprint Aug 2026

Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement

This work proposes Preference Optimization with Locally Augmented Refinement with Locally Augmented Refinement (POLAR), a novel training algorithm that applies a local search refinement pass to the best decoded tour before forming preference pairs, yielding much more informative pairwise margins.

Arthur Corrêa, P. Nascimento, Samuel Moniz · 0 citations