Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment

2026-02-16

AI Summary

This work proposes a novel framework that decouples alignment artifact learning from policy optimization to address the limitations of existing AI alignment methods, which tightly couple safety objectives with policies, resulting in non-reusable and non-auditable alignment outcomes and consequent resource inefficiency. By leveraging a model-agnostic, inspectable, and editable reward model, the framework enables persistent alignment and establishes a human-in-the-loop alignment flywheel that iteratively refines the reward model over time. For the first time, it achieves full decoupling between alignment artifacts and policy training, integrating inverse reinforcement learning, automated auditing, and data-driven techniques to produce transferable and verifiable reward models. This approach significantly enhances the transparency, reusability, and long-term reliability of the alignment process.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

AI alignment is growing in importance, yet current approaches suffer from a critical structural flaw that entangles the safety objectives with the agent's policy. Methods such as Reinforcement Learning from Human Feedback and Direct Preference Optimization create opaque, single-use alignment artifacts, which we term Alignment Waste. We propose Interactionless Inverse Reinforcement Learning to decouple alignment artifact learning from policy optimization, producing an inspectable, editable, and model-agnostic reward model. Additionally, we introduce the Alignment Flywheel, a human-in-the-loop lifecycle that iteratively hardens the reward model through automated audits and refinement. This architecture transforms safety from a disposable expense into a durable, verifiable engineering asset.

Problem

• AI alignment
• Alignment Waste
• reward model
• policy optimization
• human-in-the-loop

Innovation

• Interactionless Inverse Reinforcement Learning
• Alignment Waste
• Reward Model Decoupling
• Alignment Flywheel
• Model-Agnostic Alignment