Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models at ICLR 2026.
Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on knowledge-intensive tasks. In this paper, we formalize fact memorization from an information-theoretic perspective and study how training data distributions affect fact accuracy. We show that fact accuracy is suboptimal (below the capacity limit) whenever the amount of information contained in the training data facts exceeds model…