Disentangling generalization and memorization in large language models using chess

📰 ArXiv cs.AI

Learn to distinguish between generalization and memorization in large language models using chess as a testbed, and why this matters for AI development

advanced Published 20 May 2026

Action Steps

Apply chess as a testbed to evaluate large language models' generalization and memorization capabilities
Construct a taxonomy of chess positions with varying densities of relevant priors
Use scalable engine evaluations to assess model performance on these positions
Analyze the results to disentangle generalization and memorization in large language models
Configure experiments to test the models' ability to reason and recall

Who Needs to Know This

AI researchers and engineers can benefit from this study to improve the reasoning capabilities of large language models, while data scientists and machine learning engineers can apply the insights to other domains

Key Insight

💡 Chess can be used as a controlled testbed to disentangle generalization and memorization in large language models, providing insights into their reasoning capabilities

Key Takeaways

Learn to distinguish between generalization and memorization in large language models using chess as a testbed, and why this matters for AI development

Full Article

Title: Disentangling generalization and memorization in large language models using chess

Abstract:
arXiv:2601.16823v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit remarkable capabilities, yet it remains unclear to what extent these reflect sophisticated recall or genuine reasoning ability. We introduce chess as a controlled testbed aimed at disentangling these faculties. Leveraging the game's structure and scalable engine evaluations, we construct a taxonomy of positions varying in density of relevant priors - ranging from common states solvable by memorization

Read full paper → ← Back to Reads