Alignment via Generalization

The paper introduces Aligned‑PAC learning, a framework that reframes the alignment problem as a question of generalization: whether success on training requirements extends to unseen ones. It proposes representing intentions through multiple specific requirements, provides theoretical results on independent evaluation and the amount of evaluation needed, and develops an optimization method that combines learned models to satisfy these requirements under certain assumptions. The authors acknowledge that selecting appropriate requirements and ensuring generalization to new ones remain open challenges, positioning their work as a foundation for precise, learning‑theoretic study of alignment.

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