This paper addresses the critical challenges of aligning superhuman artificial intelligence (AI) with human values, specifically focusing on scalable oversight and the dynamic nature of these values. The authors argue that existing approaches, such as recursive reward modelling, which aim for scalable oversight, often remove humans from the alignment loop entirely, failing to account for the evolving nature of human preferences. To counter this, the paper proposes a novel algorithmic framework inspired by Iterated Amplification. This framework trains a superhuman reasoning model to decompose complex tasks into subtasks that can be evaluated and solved by aligned human-level AI. The central assumption of this approach is the "part-to-complete generalization hypothesis," which posits that the alignment of subtask solutions will generalize to the alignment of the complete solution. The paper outlines the proposed algorithm, discusses methods for measuring and improving this generalization, and reflects on how this framework addresses key challenges in AI alignment.