Produced As Part Of The SERI ML Alignment Theory Scholars Program 2022 Under John Wentworth

Introduction

This post works off the assumption that the first AGI comes relatively soon, and has an architecture which looks basically like EfficientZero, with a few improvements: a significantly larger world model and a significantly more capable search process. How would we align such an AGI?

Our pitch is to identify human values within the AGI’s world model, and use this to direct the policy selector through IRL (inverse reinforcement learning). We take a lot of inspiration from Vanessa’s PreDCA proposal [comment][video], as well as ideas developed in Infra-Bayesian Physicalism. We have stripped these down to what we saw as the core insights, meaning that there are significant differences between this and PreDCA. We initially arrived at this proposal by thinking about an idea similar to "retarget the search", except we’re using hard-coded search instead of learned optimizers, and doing the "identify human values" part using a mathematical definition of agents & goals.

We think that this proposal directly gets at what we view as the core of the alignment problem: pointing to human values in a way that is robust as capabilities scale. Naturally this all depends on the research outlined in the 'Research Required' section succeeding. See the last few sections to see many of the difficulties of this approach.

Architecture assumptions

The most important assumption that we are making is that the agent is designed to explicitly search over actions that maximize a utility function. This is opposed to the model where an AGI is a single component trained end-to-end by RL (or self-supervised learning), and where the AGI learns its own mesa-objective (or the mesa-objectives of simulacra^[1]) internally. We will lay out a concrete vision of this model, but keep in mind that the exact details don't matter much.^[2]

We are also structuring this proposal to point this agent to maximize the preferences of a single human (henceforth "the operator"), as a simplifying assumption, because we think the difficulty of this proposal is getting it to work for a single human, and scaling to recognize many people's preferences and aggregate them is not too hard.

IRL training procedure for an aligned EfficientZero style model

We train a generative world model. Thi

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