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Neural solvers based on attention mechanism have demonstrated remarkableeffectiveness in solving vehicle routing problems. However, in thegeneralization process from small scale to large scale, we find a phenomenon ofthe dispersion of attention scores in existing neural solvers, which leads topoor performance. To address this issue, this paper proposes a distance-awareattention reshaping method, assisting neural solvers in solving large-scalevehicle routing problems. Specifically, without the need for additionaltraining, we utilize the Euclidean distance information between current nodesto adjust attention scores. This enables a neural solver trained on small-scaleinstances to make rational choices when solving a large-scale problem.Experimental results show that the proposed method significantly outperformsexisting state-of-the-art neural solvers on the large-scale CVRPLib dataset.

Distance-aware Attention Reshaping: Enhance Generalization of Neural Solver for Large-scale Vehicle Routing Problems