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arXiv CS.AI·1d ago·5 min read
Size Generalization for Diffusion Routing Solvers
TL;DR
ResizeCO is a novel size-generalization approach for diffusion routing solvers that adapts models trained on small instances to efficiently solve larger routing problems. It sets a new state of the art in solving TSP and CVRP instances with up to 10,000 nodes, outperforming existing learned solvers.
✦ Why It Matters
Implement ResizeCO to enhance routing solution efficiency for large-scale problems in your projects today.
Key Takeaways
- Explore using ResizeCO for large routing problems.
- Consider reinforcement learning techniques for model adaptation.
- Utilize GPU resources for efficient computation.
- Benchmark against existing solvers to validate improvements.
How It Works
ResizeCO employs reinforcement learning to adapt a diffusion model trained on smaller routing instances. It uses route length as a training signal and formulates the k-opt optimization as a tree search, which is efficiently solved using beam search on GPUs.
This allows the model to learn from larger instances while maintaining the benefits of its training on smaller data.