Superalignment with Dynamic Human Values
This addresses the alignment problem for superhuman AI systems, but it is incremental as it builds on existing solutions like recursive reward modeling.
The paper tackles the challenges of scalable oversight and dynamic human values in AI alignment by proposing a roadmap for a novel algorithmic framework that trains a superhuman reasoning model to decompose complex tasks into subtasks for human-level guidance, based on the part-to-complete generalization hypothesis.
Two core challenges of alignment are 1) scalable oversight and 2) accounting for the dynamic nature of human values. While solutions like recursive reward modeling address 1), they do not simultaneously account for 2). We sketch a roadmap for a novel algorithmic framework that trains a superhuman reasoning model to decompose complex tasks into subtasks that are still amenable to human-level guidance. Our approach relies on what we call the part-to-complete generalization hypothesis, which states that the alignment of subtask solutions generalizes to the alignment of complete solutions. We advocate for the need to measure this generalization and propose ways to improve it in the future.