I am excited to share a work we did in the Discovery team at @GoogleDeepMind using RL and generative models to discover creative chess puzzles 🔊♟️♟️ #neurips2025 🎨While strong chess players intuitively recognize the beauty of a position, articulating the precise elements that constitute creativity remains elusive. To address this, we pre-trained generative models on public datasets and then applied reinforcement learning, using novel rewards designed for uniqueness, counter-intuitiveness, realism, and novelty. This approach doubled the number of novel chess puzzles compared to the original training data, while successfully maintaining aesthetic diversity. Three distinguished experts—International Master of chess compositions Amatzia Avni (author of "Creative Chess"), Grandmaster Jonathan Levitt @JonathanLevitt7 (author of "Secrets of Spectacular Chess"), and Grandmaster Matthew Sadler @gmmds (author of "Game Changer")—evaluated and selected the puzzles they found most compelling. Their preference was for puzzles exhibiting original, paradoxical, surprising, and naturally occurring positions, with particular emphasis on those that integrated aesthetic themes in innovative ways and demonstrated exceptional over-the-board vision. 🧩Try to solve the puzzles @chesscom: chess.com/c/2wCTN7Uv2