We expect that an aligned (blue-cloud) model would have an incentive to preserve its goals, though it would need some help from us to generalize them correctly to avoid becoming a misaligned (red-cloud) model. We talk about this in more detail in Refining the Sharp Left Turn (part 2). 

Just added some more detail on this to the slides. The idea is that we have various advantages over the model during the training process: we can restart the search, examine and change beliefs and goals using interpretability techniques, choose exactly what data the model sees, etc.

Thanks Alex for the detailed feedback! I have updated the post to fix these errors. 

Curious if you have high-level thoughts about the post and whether these definitions have been useful in your work. 

This post provides a maximally clear and simple explanation of a complex alignment scheme. I read the original "learning the prior" post a few times but found it hard to follow. I only understood how the imitative generalization scheme works after reading this post (the examples and diagrams and clear structure helped a lot). 

This post helped me understand the motivation for the Finite Factored Sets work, which I was confused about for a while. The framing of agency as time travel is a great intuition pump. 

I like this research agenda because it provides a rigorous framing for thinking about inductive biases for agency and gives detailed and actionable advice for making progress on this problem. I think this is one of the most useful research directions in alignment foundations since it is directly applicable to ML-based AI systems. 

+1. This section follows naturally from the rest of the article, and I don't see why it's labeled as an appendix -  this seems like it would unnecessarily discourage people from reading it. 

It's great to hear that you have updated away from ambitious value learning towards corrigibility-like targets. It sounds like you now find it plausible that corrigibility will be a natural concept in the AI's ontology, despite it being incompatible with expected utility maximization. Does this mean that you expect we will be able to build advanced AI that doesn't become an expected utility maximizer?

I'm also curious how optimistic you are about the interpretability field being able to solve the empirical side of the abstraction problem in the next 5-10 years. Current interpretability work is focused on low-level abstractions (e.g. identifying how a model represents basic facts about the world) and extending the current approaches to higher-level abstractions seems hard. Do you think the current interpretability approaches will basically get us there or will we need qualitatively different methods? 

I would consider goal generalization as a component of goal preservation, and I agree this is a significant challenge for this plan. If the model is sufficiently aligned to the goal of being helpful to humans, then I would expect it would want to get feedback about how to generalize the goals correctly when it encounters ontological shifts. 

I agree that a sudden gain in capabilities can make a simulated agent undergo a sharp left turn (coming up with more effective takeover plans is a great example). My original question was about whether the simulator itself could undergo a sharp left turn. My current understanding is that a pure simulator would not become misaligned if its capabilities suddenly increase because it remains myopic, so we only have to worry about a sharp left turn for simulated agents rather than the simulator itself. Of course, in practice, language models are often fine-tuned with RL, which creates agentic incentives on the simulator level as well. 

You make a good point about the difficulty of identifying dangerous models if the danger is triggered by very specific prompts. I think this may go both ways though, by making it difficult for a simulated agent to execute a chain of dangerous behaviors, which could be interrupted by certain inputs from the user.