Life & fun · December 29, 2025
Per-instance TSP Solver with No Pre-training (1.66% gap on d1291)
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of…
In plain words
This is a traveling salesman problem solver that learns independently for each problem instance using reinforcement learning, without relying on pre-trained models from other datasets. Built with PPO, it achieved a 1.66% optimality gap on a standard benchmark instance, solving it in about 5.6 hours on a single GPU. The approach is designed for researchers and practitioners working on combinatorial optimization who want to explore instance-specific learning strategies that leverage geometric structure rather than cross-problem patterns.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
OP here. Most Deep Learning approaches for TSP rely on pre-training with large-scale datasets. I wanted to see if a solver could learn "on the fly" for a specific instance without any priors from other problems. I built a solver using PPO that learns from scratch per instance. It achieved a 1.66% gap on TSPLIB d1291 in about 5.6 hours on a single A100. The Core Idea: My hypothesis was that while optimal solutions are mostly composed of 'minimum edges' (nearest neighbors), the actual difficulty comes from a small number of 'exception edges' outside of that local scope. Instead of pre-training, I designed an inductive bias based on the topological/geometric structure of these exception edges. The agent receives guides on which edges are likely promising based on micro/macro structures, and PPO fills in the gaps through trial and error. It is interesting to see RL reach this level without a dataset. I have open-sourced the code and a Colab notebook for anyone who wants to verify the results or tinker with the 'exception edge' hypothesis. Code & Colab: https://github.com/jivaprime/TSP_exception-edge Happy to answer any questions about the geometric priors or the PPO implementation!
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Dev tools · Dec 2025 · hackerbook.dosaygo.com
