Ataraxos beats Stratego's top player 15-1-4 at a fraction of DeepNash's compute (Nature)
On Sept 30, 2026 Nature published Ataraxos, a Stratego AI from researchers at MIT, CMU, NYU and Stanford led by MIT's Gabriele Farina. It beat Pim Niemeijer, the game's most decorated player, 15 wins, 1 loss and 4 draws, and went about 38–39 wins to 2 losses against elite players at a world championship exhibition. It combines self-play RL with decision-time planning over a learned belief model of hidden pieces. It beat DeepMind's DeepNash using under 1% of its training examples and a few thousand dollars of GPU time.
Key facts
- Paper: 'Scalable decision-making for games of imperfect information', Nature, Sept 30, 2026 (preprint on arXiv Nov 2025)
- Lead: Gabriele Farina (MIT EECS); collaborators at Carnegie Mellon, NYU and Stanford
- Vs Pim Niemeijer: 15 wins, 1 loss, 4 draws over 20 games
- World-championship exhibition vs elite players: reported as 38-2 (MIT News via SiliconSnark) or 39-2 (other reports)
- Reaches 'strictly higher playing strength than DeepNash while using less than one hundredth of the training examples'; the paper estimates DeepNash would cost $3–4.5M to train today
- Training: 16 NVIDIA H100s for about a week, plus 4 H100s for 4 days for the belief network (reported)
- Also superhuman Barrage Stratego, and state-of-the-art Hanabi and dou dizhu results
What happened
Stratego hides each piece's identity, which makes classic game-tree search hard. DeepMind's DeepNash (2022) reached expert level with huge compute and no search. Ataraxos instead plans at decision time over sampled hidden states from a generative belief network, and needs far less training.
Why it matters
It shows how cheap superhuman play in a large imperfect-information game has become. Explicit belief modelling plus search beats brute-force self-play, a lesson relevant to agents that act under hidden information.
Unverified: the exhibition record differs between reports (38-2 vs 39-2); the Nature paper itself was paywalled in this run.
Changelog
- 2026-10-01: created
Sources (3)
- paperNature: Scalable decision-making for games of imperfect information
- pressSiliconSnark: MIT's Ataraxos beats Stratego champions
- pressAI Weekly: Ataraxos AI tops Stratego champion for a few thousand dollars
id: 2026-09-30-ataraxos-stratego-nature · updated 2026-10-01 · open in the interactive timeline