A Solvable Theory of Pre-training Data Poisoning: Regime-Dependent Scaling Exponents

Pre-training data poisoning of large language models is usually studied using targeted backdoors and their survival through safety post-training, which leaves open a more basic question: how does a model's clean data performance degrade as the poison rate $\varepsilon$ grows? Motivated by our contro