arXiv AI October 1, 2026 By Aatu Selkee, Severi Rissanen, Xidong Feng, Tom Zahavy, Eric Malmi

Conditional Generation of Creative Chess Puzzles with Diffusion Models

The paper presents a method for generating creative chess puzzles using masked diffusion models that can be conditioned on tactical themes and partial board positions. It introduces an auxiliary best‑move prediction task that boosts solution uniqueness by 11.6% and theme‑conditioning accuracy by 2.5%. A reinforcement learning framework further increases the yield of unique, theme‑matching puzzles by 89.1%, and the authors release open‑weights models for the community.

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llms diffusion reinforcement-learning benchmarks

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