NPO: Learning Alignment and Meta-Alignment through Structured Human Feedback

TL;DR

NPO introduces a structured feedback framework for continuous alignment and meta-alignment learning in human-in-the-loop systems, ensuring measurable, supervisable, and convergent adaptation.

Contribution

It formalizes alignment and meta-alignment as measurable, reducible properties, and provides a scalable operational loop with convergence guarantees.

Findings

NPO achieves measurable alignment improvements in large-scale deployments.

Formal convergence of alignment and monitoring fidelity under stochastic feedback.

Simulation studies validate the theoretical principles and practical effectiveness.

Abstract

We present NPO, an alignment-aware learning framework that operationalizes feedback-driven adaptation in human-in-the-loop decision systems. Unlike prior approaches that treat alignment as a static or post-hoc property, NPO introduces a formalization of alignment loss that is measurable, supervisable, and reducible under structured feedback. In parallel, we propose meta-alignment as the fidelity of the monitoring process that governs retraining or override triggers, and show that it is formally reducible to primary alignment via threshold fidelity. Our implementation spans a scalable operational loop involving scenario scoring, threshold tuning, policy validation, and structured feedback ingestion, including "likes", overrides, and abstentions. We provide formal convergence results under stochastic feedback and show that both alignment loss and monitoring fidelity converge additively. Empirically, NPO demonstrates measurable value in hyperscale deployment settings. A simulation-based artifact and ablation studies further illustrate the theoretical principles in action. Together, NPO offers a compact, inspectable architecture for continual alignment monitoring, helping bridge theoretical alignment guarantees with practical reliability in dynamic environments.