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technology3h ago
Figuring out why AIs get flummoxed by some games
- New research shows self-play training struggles on Nim when board size increases, revealing limits of AlphaZero-style learning.
- On a seven-row Nim board, gains from training largely stopped after 500 iterations, signaling limited learning capacity.
- Replacing the move evaluator with randomness produced similar results, showing the AI couldn't learn the parity function from outcomes alone.
- Zhou and Riis conclude Nim requires learning the parity function, which AlphaZero-like training cannot reliably provide.
- The study warns that similar issues could appear in chess-playing AIs, where long-mrange sequences are hard to evaluate early.
- Researchers suggest a potential gap between AlphaZero-style learning and the symbolic reasoning needed for general rules.
- The findings have implications for AI use in math problems that rely on symbolic reasoning and generalization.
- The paper appears in Machine Learning 2026, with DOI 10.1007/s10994-026-06996-1.
- The authors are Bei Zhou and Soren Riis, highlighting the need for symbolic reasoning in AI game research.
- The study connects the weaknesses to broader AI training paradigms used for math problems and symbolic tasks.
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