A First Guess is Rarely the Final Answer: Learning to Search in the Traveling Salesperson Problem
Most neural solvers for the Traveling Salesperson Problem (TSP) are trained to output a single solution, even though practitioners rarely stop there: at test time, they routinely spend extra compute on sampling or post-hoc search. This raises a natural question: can the search procedure itself be learned? Neural improvement methods take this perspective by learning a policy that applies local modifications to a candidate solution, accumulating gains over an improvement trajectory. Yet learned improvement for TSP remains comparatively immature, with existing methods still falling short of robust, scalable performance. We argue that a key reason is design mismatch: many approaches reuse sta...
| Authors | Andoni Irazusta Garmendia |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 1 May 2026 |
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