dx26

This post was produced as part of the Astra Fellowship under the Winter 2024 Cohort, mentored by Richard Ngo. Thanks to Martín Soto, Jeremy Gillen, Daniel Kokotajlo, and Lukas Berglund for feedback. Summary Discussions around the likelihood and threat models of AI existential risk (x-risk) often hinge on some informal concept of a “coherent”, goal-directed AGI in the future maximizing some utility function unaligned with human values. Whether and how coherence may develop in future AI systems, especially in the era of LLMs, has been a subject of considerable debate. In this post, we provide a preliminary mathematical definition of the coherence of a policy as how likely it is to have been sampled via uniform reward sampling (URS), or uniformly sampling a reward function and then sampling from the set of policies optimal for that reward function, versus uniform policy sampling (UPS). We provide extensions of the model for sub-optimality and for “simple” reward functions via uniform sparsity sampling (USS). We then build a classifier for the coherence of policies in small deterministic MDPs, and find that properties of the MDP and policy, like the number of self-loops that the policy takes, are predictive of coherence when used as features for the classifier. Moreover, coherent policies tend to preserve optionality, navigate toward high-reward areas of the MDP, and have other “agentic” properties. We hope that our metric can be iterated upon to achieve better definitions of coherence and a better understanding of what properties dangerous AIs will have. Introduction Much of the current discussion about AI x-risk centers around “agentic”, goal-directed AIs having misaligned goals. For instance, one of the most dangerous possibilities being discussed is of mesa-optimizers developing within superhuman models, leading to scheming behavior and deceptive alignment. A significant proportion of current alignment work focuses on detecting, analyzing (e.g. via analogous cas