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[2602.14844] Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment
Summary
This paper introduces Interactionless Inverse Reinforcement Learning, a framework aimed at improving AI alignment by decoupling safety objectives from policy optimization, thereby creating a more durable and verifiable reward model.
Why It Matters
As AI systems become more integrated into critical applications, ensuring their alignment with human values is essential. This framework addresses current limitations in AI alignment methods, which often produce one-time solutions that are not robust or easily adjustable. By proposing a more sustainable approach, it enhances the safety and reliability of AI systems.
Key Takeaways
- Current AI alignment methods create alignment waste, hindering long-term safety.
- The proposed framework allows for an inspectable and editable reward model.
- A human-in-the-loop lifecycle enhances the durability of AI safety measures.
- Decoupling alignment from policy optimization leads to more robust AI systems.
- This approach transforms safety from a disposable expense to a verifiable asset.
Computer Science > Machine Learning arXiv:2602.14844 (cs) [Submitted on 16 Feb 2026] Title:Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment Authors:Elias Malomgré, Pieter Simoens View a PDF of the paper titled Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment, by Elias Malomgr\'e and 1 other authors View PDF HTML (experimental) 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. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.14844 [cs.LG] (or arXiv:2602.14844v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.14844 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history F...