Superhuman AI for Stratego
Computer Science > Machine Learning arXiv:2511.07312 (cs) [Submitted on 10 Nov 2025] Title:Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search Authors:Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, J. Among these, Stratego -- a board wargame exemplifying the challenge of strategic decision making under massive amounts of hidden information -- stands apart as a case where such efforts failed to produce performance at the level of top humans. This work establishes a step change in both performance and cost for Stratego, showing that it is now possible not only to reach the level of top humans, but to achieve vastly superhuman level -- and that doing so requires not an industrial budget, but merely a few thousand dollars.
- ▪Computer Science > Machine Learning arXiv:2511.07312 (cs) [Submitted on 10 Nov 2025] Title:Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search Authors:Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, J.
- ▪Among these, Stratego -- a board wargame exemplifying the challenge of strategic decision making under massive amounts of hidden information -- stands apart as a case where such efforts failed to produce performance at the level of top huma
- ▪This work establishes a step change in both performance and cost for Stratego, showing that it is now possible not only to reach the level of top humans, but to achieve vastly superhuman level -- and that doing so requires not an industrial
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2511.07312 |
| Publication time | Fri, 02 Oct 2026 00:27:51 +0000 |
| Retrieval time | 2026-10-02T00:32:55.368Z |
| Last seen | 2026-10-02T00:32:55.368Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2511.07312 (cs) [Submitted on 10 Nov 2025] Title:Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search Authors:Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, J. Zico Kolter, Gabriele Farina View a PDF of the paper titled Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search, by Samuel Sokota and 4 other authors View PDF HTML (experimental) Abstract:Few classical games have been regarded as such significant benchmarks of artificial intelligence as to have justified training costs in the millions of dollars.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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