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Ataraxos AI Beats Stratego's Most Decorated Player 15 Wins to 1 in Nature Paper, on a Training Run Costing Under $8,000

Researchers from CMU, NYU, Stanford and MIT report in Nature that Ataraxos beat Pim Niemeijer with 15 wins, 1 loss and 4 draws, using far less compute than DeepMind's DeepNash.

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Overview

An AI system called Ataraxos has defeated the most decorated human Stratego player in a 20-game series, according to a paper published in Nature on 30 September 2026. The paper, titled “Scalable decision-making for games of imperfect information,” is by Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, Zhiyuan Fan, J. Zico Kolter and Gabriele Farina, with affiliations at Carnegie Mellon University, New York University, Stanford University and the Massachusetts Institute of Technology.

What We Know

The match. Per Nature, Ataraxos defeated Pim Niemeijer, described as the most decorated Stratego player of all time, with 15 wins, 1 loss and 4 draws. The authors put the margin at an 85% effective win rate, counting draws as half wins, and write that it is “without precedent at the highest level of human play.” They describe the result as, “to our knowledge, the first superhuman result in the game’s history.” The paper says the series was spread over 3 weeks, and that Niemeijer was informed Ataraxos would not adapt to his play, so he could adjust over the series while the AI could not. Niemeijer was paid US$1,000 for taking part, plus US$100 per win and US$50 per draw, according to the paper.

Further games. After the series, the authors demoed Ataraxos at the 2025 Stratego World Championship from 1 to 3 August. Nature reports a 95% effective win rate across 40 games there (38 wins, 2 losses, 0 draws). The same techniques were applied to three other games. In Barrage Stratego, a variant, the paper says Ataraxos won each of four 50-game series against top-ranked human players. It also reports state-of-the-art results in Hanabi, a cooperative game, and in dou dizhu, where it “defeated PerfectDou and DouZero with statistical significance.”

How it works. The paper describes a design pattern with three parts: a policy-value network trained through self-play, a belief network that models hidden information, and a search procedure that refines the policy at test time. Nature says the approach uses stronger regularization and more aggressive policy updates early in self-play, and weaker regularization and smaller updates late.

Cost. The reinforcement learning run used 16 NVIDIA H100 GPUs for 1 week, and the belief network run used 4 H100s for 4 days. The authors write that such a run costs less than US$8,000 at 2025 prices, per Nature. They estimate that DeepNash, the earlier DeepMind system, would cost roughly US$3,000,000 to US$4,500,000 under 2025 pricing, based on 1,024 tensor processing unit nodes running for between 2 and 3 months. The paper also says DeepNash consumed about 5.5 billion games, against about 160 million for Ataraxos.

The predecessor. Google DeepMind announced DeepNash in December 2022, describing an agent that learned Stratego from scratch to a human expert level and reached an all-time top-three ranking on the online platform Gravon. The Ataraxos paper adds caveats about that evaluation: per Nature, DeepNash was evaluated in April 2022, winning 42 of 50 counted games without achieving the top ranking, and it lost to most of the highest-ranked players it met in a 2023 championship demo, including Niemeijer.

What We Don’t Know

  • The authors note that game outcomes against a human who adapts are not independent, so the statistical test they cite (a one-sided binomial test with a P value below 2.6 × 10^-4) is given “under the assumption that they had been” independent and identically distributed.
  • A direct Ataraxos-DeepNash match is not possible. Nature says the authors asked DeepMind, which responded that the DeepNash code “is no longer functional.” The cost comparison therefore rests on the authors’ own estimates and the recollection of DeepNash’s corresponding author about training duration.
  • Whether the techniques carry over to real-world settings with hidden information is not tested in the paper; its evidence comes from the four games above.

Analysis

The paper’s main claim is about method as much as the match: that reinforcement learning and search “are no longer precluded from high performance by the presence of large amounts of hidden information.” The reported training bill of under US$8,000 would put such results within reach of a university lab, though the headline comparison depends on the authors’ price estimates for hardware used years apart.