  Vehicle Routing Problems (VRPs) are optimization problems with significant
real-world implications in logistics, transportation, and supply chain
management. Despite the recent progress made in learning to solve individual
VRP variants, there is a lack of a unified approach that can effectively tackle
a wide range of tasks, which is crucial for real-world impact. This paper
introduces RouteFinder, a framework for developing foundation models for VRPs.
Our key idea is that a foundation model for VRPs should be able to model
variants by treating each variant as a subset of a larger VRP problem, equipped
with different attributes. We introduce a parallelized environment that can
handle any combination of attributes at the same time in a batched manner, and
an efficient sampling procedure to train on a mix of problems at each
optimization step that can greatly improve convergence robustness. We also
introduce novel Global Feature Embeddings that project instance-wise attributes
efficiently onto the latent space and help the model understand different VRP
variants. Finally, we introduce Efficient Adapter Layers, a simple yet
effective technique to finetune pre-trained RouteFinder models to solve novel
variants with previously unseen attributes outside of the original feature
space. We validate our approach through extensive experiments on 24 VRP
variants, demonstrating competitive results over recent multi-task learning
models. We make our code openly available at
https://github.com/ai4co/routefinder.
