Nim reveals the limits of AlphaZero-style AI training
A study shows AlphaZero-style self-play that excels at chess and Go falters on Nim, an impartial game, because winning depends on learning a parity function rather than pattern-based associations. As Nim boards grow, the AI’s improvements stall or collapse, even with random exploration, indicating a fundamental limitation of current self-play training for tasks requiring symbolic reasoning. The finding raises concerns about applying such training to math problems and other rule-based challenges, highlighting a tangible failure mode in AI learning.
