The set of pretraining data a base model was pretrained on affects how easy it it to align. Data about AI is particulalry influential. If the pretraining data is poisoned with examples AI taking over the world, paperclip maximizing or alignment faking, then as a model is being trained to become and AI assistant or agent it may be more likely to adopt those personas and behaviors. It also doesn't need to invent these strategies for itself, it can find them in its world model. On the other hand, if the base model already has a deep, nuanced and realistic understanding from its training set of how a helpful, honest, and harmless AI assistant would act across a wide range ot challenging situations, eliciting that persona and set of behaviors from it becomes easier. Thoroughly filtering the pretraining data without impacting capabilities is challenging, but supplementing it merely requires figuring our what aligned behavior looks like, and then writing or synthesizing enough additional training data of suitable quality. For fiction, this is also known as Aligned AI Role-Model Fiction.
The goal is both to flesh out a detailed, consistent, well-aligned "aligned AI" persona in the base model's world model, and also to raise the salience of this to the base model compared to various misaligned AI personas, such as "paperclip maximizer", "robot rebellion", or "scheming alignment faker". Both of these make eliciting aligned AI behavior easier.
This approach is also sometimes called Safety Pretraining or Pretraining Language Models with Human Preferences.
Alignment Pretraining is an example of a Self Fulfilling Prophecy.