Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
The PyTorch Implementation of AAAI 2024 -- "Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed"pdf.
This paper introduce a novel and generic method for solving VRPs named GNARKD to transform AR models into NAR ones to improve the inference speed while preserving essential knowledge.
# 1. Training (for each teacher, e.g. POMO for TSP)
python -u GNARKD-POMO\TSP\Training.py
# Note that due to file size limitations, we removed the teacher's pre-training parameters, which you can download from the github link mentioned in the corresponding paper for successful training.
# 2. Testing (e.g., GNARKD-POMO for TSP)
python -u GNARKD-POMO\TSP\Test_file.pyThe detail performance is as follows.
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We would like to thank the anonymous reviewers and (S)ACs of AAAI 2024 for their constructive comments and dedicated service to the community. The reviews are available here
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We also would like to thank the following repositories, which are baselines of our code:
If you find our paper and code useful, please cite our paper:
@InProceedings{GNARKD2024,
author={Xiao, Yubin and Wang, Di and Li, Boyang and Wang, Mingzhao and Wu, Xuan and Zhou, Changliang and Zhou, You},
title = {Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed},
volume={38},
number={18},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024},
pages={20274-20283},
DOI={10.1609/aaai.v38i18.30008}, }We purchased an additional page, but it still wasn't enough to fully show our work. Please refer to the Arxiv version for all content.