We unpack a DeepMind study on training an AI to generate genuinely creative chess puzzles, using reinforcement learning and a three-part creativity framework: uniqueness, novelty, and counterintuitiveness via a 'search gap' between shallow and deep engine evaluations. Stockfish ensures a unique solution; Levenshtein distance enforces novelty in both the puzzle and the solution; and a diversity filter guards against reward hacking. Results showed a tenfold rise in counterintuitive puzzles (0.22% baseline to 2.5%), surpassing human puzzle rates in a large dataset. Expert reviewers found the AI puzzles more creative and enjoyable. We discuss broader implications: a general blueprint for nurturing AI creativity in domains like Go, mathematics, and even prompting large language models, turning shallow intuition into deeper, surprising insight.

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