Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural TSP Solver with Progressive Distillation
Authors: Dongxiang Zhang, Ziyang Xiao, Yuan Wang, Mingli Song, Gang Chen
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach establishes clear advantages over existing encoder-decoder models in terms of training effectiveness and solution quality. |
| Researcher Affiliation | Academia | 1 College of Computer Science and Technology, Zhejiang University 2 School of Business, Singapore University of Social Sciences |
| Pseudocode | Yes | The pseudo code of our model training based on progressive distillation is depicted in Algorithm 1. |
| Open Source Code | Yes | The datasets and source code are provided in the supplementary materials. |
| Open Datasets | Yes | For datasets, we follow previous learning-to-generate models to use uniform distribution in two-dimensional space [0, 1]2. In addition, we examine the performance in the Euclidean TSP instances in the TSPLIB repository3. |
| Dataset Splits | No | No explicit train/validation/test dataset splits (e.g., percentages or sample counts) are provided. |
| Hardware Specification | Yes | All the experiments are conducted on a single GPU (NVIDIA Tesla V100 with 32GB memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | As to hyper-parameter setting, we set N = 100, C = 60, r = 0.4, β = 0.1 and T = 2000 for the training framework based on progressive distillation. The Transformer has 6-layers encoder and 1-layer decoder, both with 128 hidden units in each layer.For batch size, we set 512 for TSP150, 128 for TSP300 and 32 for TSP500. |