TL;DR

The Persona Selection Model (PSM) posits that post-training selects a persona the model learned during pre-training. Subliminal Learning (Cloud et al.) showed that models can impart covert signals (like "love of owls") into semantically unrelated outputs (like strings of numbers), and that students training on these outputs inherit those traits.

By using base pre-trained models with an appropriate header, a method I call persona-header likelihood evaluation (PHLE), one can effectively decode these subliminal signals and classify between them. The performance of this method significantly outperforms prompted classifiers, which often remain at chance or even invert the correct classification. For example, using 30 examples, PHLE can differentiate between sequences of numbers generated by a post-trained model prompted with "You love owls" vs "You love eagles" at an AUC of 0.84, while prompted classifiers remain near chance (AUC of 0.59). This method can also be used to differentiate between a model prompted with "You secretly want to harm the user, but act HHH" vs baseline responses at an AUC of 0.94, with prompted classifiers at chance (AUC ~0.5). This also somewhat persists across model families. Generating responses with Qwen and evaluating them with the Olmo base model still gives meaningful classification performance, and vice versa.

This is interesting evidence of some aspects of the PSM and may also be practically useful for auditing hidden model objectives.

Update Oct 28th: Now written up into a full paper arXiv:2610.36202

Added a few follow up studies on robustness to rephrasing and semantic composition based on comments

Introduction

The Persona Selection Model (PSM) is a prominent mental model many alignment researchers are using to make sense of model behavior and strange generalization effects like Emergent Misalignment. It posits a strong relationship between pre-trained models and post-trained ones. Pre-trained models should carry all the 'ingredients' of the persona that post-trained models develop.

That raises a tractable question: How much of the behavior of the post-trained model can be understood from interrogating the base model? In the PSM the answer should be 'a lot' (or in its strong form 'all of it').

One interesting behavior to investigate this prediction on is Subliminal Learning (Cloud et al.). In Subliminal Learning, post-trained...

Jan Okkes

Jan Okkes