For approximately the past year, I’ve been doing alignment research full-time, working on a variety of approaches, and trying to understand the problem in-depth enough to invent new ones. If funded, I plan to continue doing approximately the same work as before, which has historically been scalable mechanistic interpretability, formal and prosaic corrigibility, reflective stability, and a bunch of value theory stuff. Along with lots of upskilling in convex optimization, machine learning, neuroscience, and economics.

My current project is an attempt to connect the tools & theory of singular learning theory with our knowledge of the inductive biases and loss landscapes of large language models.