Generating Creative Chess Puzzles
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908616968110080 |
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| author | Feng, Xidong Veeriah, Vivek Chiam, Marcus Dennis, Michael Pachauri, Ryan Tumiel, Thomas Barbero, Federico Obando-Ceron, Johan Shi, Jiaxin Singh, Satinder Hou, Shaobo Tomašev, Nenad Zahavy, Tom |
| author_facet | Feng, Xidong Veeriah, Vivek Chiam, Marcus Dennis, Michael Pachauri, Ryan Tumiel, Thomas Barbero, Federico Obando-Ceron, Johan Shi, Jiaxin Singh, Satinder Hou, Shaobo Tomašev, Nenad Zahavy, Tom |
| contents | While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22\% (supervised) to 2.5\%, surpassing existing dataset rates (2.1\%) and the best Lichess-trained model (0.4\%). Our puzzles meet novelty and diversity benchmarks, retain aesthetic themes, and are rated by human experts as more creative, enjoyable, and counter-intuitive than composed book puzzles, even approaching classic compositions. Our final outcome is a curated booklet of these AI-generated puzzles, which is acknowledged for creativity by three world-renowned experts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Creative Chess Puzzles Feng, Xidong Veeriah, Vivek Chiam, Marcus Dennis, Michael Pachauri, Ryan Tumiel, Thomas Barbero, Federico Obando-Ceron, Johan Shi, Jiaxin Singh, Satinder Hou, Shaobo Tomašev, Nenad Zahavy, Tom Artificial Intelligence Machine Learning While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22\% (supervised) to 2.5\%, surpassing existing dataset rates (2.1\%) and the best Lichess-trained model (0.4\%). Our puzzles meet novelty and diversity benchmarks, retain aesthetic themes, and are rated by human experts as more creative, enjoyable, and counter-intuitive than composed book puzzles, even approaching classic compositions. Our final outcome is a curated booklet of these AI-generated puzzles, which is acknowledged for creativity by three world-renowned experts. |
| title | Generating Creative Chess Puzzles |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.23881 |