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.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
