Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models
The paper discusses the performance of chess-trained language models, particularly focusing on KinGPT, a 25M-parameter model. It highlights how KinGPT outperforms larger models like ChessGPT on specific chess puzzles, suggesting that high benchmark scores may stem from pattern-matching rather than true understanding. The authors propose a verifier-in-the-loop framework that significantly improves move accuracy and generation validity, offering a cost-effective alternative to traditional training methods.
- ▪KinGPT, a 25M-parameter model, outperforms larger models on chess puzzles.
- ▪The impressive performance of chess-trained language models is attributed to pattern-matching.
- ▪A verifier-in-the-loop framework enhances move accuracy and validity significantly.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17565 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17565 (cs) [Submitted on 17 May 2026] Title:Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models Authors:Ethan Tang View a PDF of the paper titled Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models, by Ethan Tang View PDF HTML (experimental) Abstract:Recent work has fine-tuned language models on chess data and reported high benchmark scores as evidence that the resulting models can understand the rules of chess, play full chess games at a professional level, or generate human-readable explanations grounded in expert knowledge.
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