Superalignment with Dynamic Human Values

2025-03-17

AI Summary

This paper addresses two core challenges in AI alignment: insufficient scalable supervision and the dynamic evolution of human values. To tackle these, we propose a “hyper-alignment” framework grounded in recursive subtask decomposition. Methodologically, it introduces the novel “part-to-whole generalization hypothesis,” positing that alignment at the subtask level serves as a generalizable, measurable, and optimizable foundation for full-task alignment. The framework integrates recursive task decomposition, human-in-the-loop subtask evaluation, and explicit modeling and quantification of alignment generalizability. By treating alignment as a hierarchical, compositional property rather than a monolithic one, it enables continuous adaptation to evolving human values while ensuring supervision scalability. Crucially, it endows superhuman reasoning models with human-controllable granularity for solving complex tasks. Our approach establishes a theoretically grounded, empirically tractable paradigm for robust alignment of superintelligent systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Address scalable oversight in AI alignment.
• Account for dynamic human values in AI.
• Develop a framework for superhuman reasoning tasks.

Innovation

• Dynamic human values integration in AI alignment
• Superhuman reasoning model for task decomposition
• Part-to-complete generalization hypothesis application