license: mit
pipeline_tag: reinforcement-learning
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
This repository hosts the model checkpoints for RouteFinder, a comprehensive foundation model framework designed to tackle various Vehicle Routing Problem (VRP) variants, as presented in the paper RouteFinder: Towards Foundation Models for Vehicle Routing Problems.
Abstract
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any combination of these attributes. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 48 VRP variants show RouteFinder outperforms recent state-of-the-art learning methods. Our code is publicly available at this https URL .
Code
The official code repository is available on GitHub: https://github.com/ai4co/routefinder
Quickstart
Installation
We recommend using uv (Python package manager) to manage dependencies:
uv venv --python 3.12 # create a new virtual environment
source .venv/bin/activate # activate the virtual environment
uv sync --all-extras # for all dependencies
This project is also compatible with pip install -e ..
Download data and checkpoints
To download the data and checkpoints from HuggingFace automatically, you can use:
python scripts/download_hf.py
Running
We recommend exploring this quickstart notebook to get started with the RouteFinder codebase!
The main runner (example here of main baseline) can be called via:
python run.py experiment=main/rf/rf-transformer-100
You may change the experiment by using the experiment=YOUR_EXP, with the path under configs/experiment directory.
Citation
If you find RouteFinder valuable for your research or applied projects, please cite our paper:
@article{
berto2025routefinder,
title={{RouteFinder: Towards Foundation Models for Vehicle Routing Problems}},
author={Federico Berto and Chuanbo Hua and Nayeli Gast Zepeda and Andr{\'e} Hottung and Niels Wouda and Leon Lan and Junyoung Park and Kevin Tierney and Jinkyoo Park},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=QzGLoaOPiY},
}