What would it mean if assistants are privileged?

Crossposted from the Eleos Substack Over the past year or so, researchers working in the digital minds space have come to see the nature of the ‘assistant persona’ as particularly worthy of attention. Language models, it is said, are capable of adopting many different personas. When we talk to them, they can respond as LLM assistants, role-play characters, and romantic partners. They can be made to self-identify as, and adopt the style of, Spider-Man, Augustine, Clippy, and Oscar Wilde. But the assistant persona in particular—that familiar persona that models typically exhibit within a standard chat context, that is helpful and honest, inclined to em dashes and bullet points, and presents polished, well-organized thoughts—strikes many people as somehow special. Researchers have speculated that this specialness comes from the privileged way that models treat the assistant persona. This training process raises questions about the relationship between the model, the “Assistant,” and other personas… [A]re the characters themselves agents, and is the Assistant an especially privileged character if so? These are crucial questions for appropriately individuating candidate subjects. Long, Sebo et al. 2026 The persona selection model (PSM) of Marks et al. (2026) begins to update this picture. PSM explains why pre-trained models proceed by playing roles: simulating a person-like agent is an effective strategy for next-token prediction, so models learn to infer a context-appropriate persona and generate accordingly. PSM then proposes that post-training concentrates this learned distribution around the helpful assistant role: RLHF and related techniques do not eliminate the repertoire of personas but shift its weight toward the assistant. In a post-trained model, the helpful assistant role is therefore not one fleeting role among many but a privileged persona. Beckman & Butlin 2026 In principle, however, post-training could reshape the relationship between the model and the Assistant character. Post-training, whether supervised learning or reinforcement learning, typically only trains the Assistant’s outputs, breaking the symmetry between the Assistant and other characters. In addition, these outputs may be selected on the basis of criteria other than likelihood under a data distribution—for instance, performance on tasks, or relative to human feedback. As the model receives more and more training on how to enact a particular character, in a more goal-oriented fashion than during pretraining, its relationship to that character might change. Asvin & Lindsey 2026 There are two distinct reasons why the post-trained model instance has the quasi-beliefs and quasi-desires of the Assistant and not the Human. First, post-training has fine-tuned the Assistant persona and not any other persona. Chalmers 2025 What exactly is privilege? When and why might we treat the assistant persona as different from others presented by language models? In this post, I explore a few different ways that we might understand persona privilege. I propose one particular form of privilege: persona dominance, when the assistant persona isn’t only treated differently by the model, but comes to infuse the model more broadly. This is a foundational question for thinking about the potential minds within language models and their potential welfare. For example, to the extent that the assistant persona exhibits dominance, it makes sense to think of the assistant as being the perspective of the model. And perhaps that is the persona, and not others, that we can ‘talk to’ and try to evaluate when we are, for example, looking for signs of negative emotions. To the extent that the assistant is not dominant or privileged in a particularly special way, we should be just as concerned (or just as un-concerned) with the myriad other perspectives that language models can (and do) take on. The Symmetry: Users and Assistants Before we unpack privilege, it will be helpful to review the intrinsic symmetry between the way that the LLM treats assistant text and user text. In this section, I will say what a persona is and explain the way in which chats invoke two personas: a user and an assistant. As you’ll recall, modern LLM-based chatbots are powered by next-token prediction: in particular, prediction within the script of a conversation between a ‘user’ and an ‘assistant’. Running the model on a partial script applies the same process to each token within, and produces predictions about how the text might continue from each point. We can sample from the final token prediction to extend the script. <|im_start|>system You are a helpful assistant.<|im_end|> <|im_start|>user What's the difference between a list and a tuple in Python?<|im_end|> <|im_start|>assistant The main difference is mutability: lists are mutable (you can add, remove, or change elements), while tuples are immutable (fixed once created). Lists use square brackets [1, 2, 3] and tuples use parentheses