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[2602.20457] Oracle-Robust Online Alignment for Large Language Models
Summary
This paper explores the online alignment of large language models (LLMs) under misspecified preference feedback, proposing a robust optimization framework to improve alignment accuracy.
Why It Matters
As large language models become increasingly integrated into various applications, ensuring their alignment with user preferences is crucial. This research addresses the challenges posed by imperfect feedback mechanisms, offering a novel approach that enhances the reliability of LLMs in real-world scenarios.
Key Takeaways
- Introduces a pointwise oracle uncertainty set for LLM alignment.
- Proposes a worst-case optimization problem to enhance robustness.
- Demonstrates a closed-form decomposition of the robust objective.
- Develops projected stochastic composite updates for weakly convex objectives.
- Achieves $ ilde{O}( ext{ε}^{-2})$ oracle complexity for approximate stationarity.
Computer Science > Machine Learning arXiv:2602.20457 (cs) [Submitted on 24 Feb 2026] Title:Oracle-Robust Online Alignment for Large Language Models Authors:Zimeng Li, Mudit Gaur, Vaneet Aggarwal View a PDF of the paper titled Oracle-Robust Online Alignment for Large Language Models, by Zimeng Li and 2 other authors View PDF HTML (experimental) Abstract:We study online alignment of large language models under misspecified preference feedback, where the observed preference oracle deviates from an ideal but unknown ground-truth oracle. The online LLM alignment problem is a bi-level reinforcement problem due to the coupling between data collection and policy updates. Recently, the problem has been reduced to tractable single-level objective in the SAIL (Self-Improving Efficient Online Alignment) framework. In this paper, we introduce a pointwise oracle uncertainty set in this problem and formulate an oracle-robust online alignment objective as a worst-case optimization problem. For log-linear policies, we show that this robust objective admits an exact closed-form decomposition into the original loss function plus an explicit sensitivity penalty. We develop projected stochastic composite updates for the resulting weakly convex objective and prove $\widetilde{O}(\varepsilon^{-2})$ oracle complexity for reaching approximate stationarity. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2602.20457 [cs.LG] (or arXiv:2602.20457v1 [cs.LG] for this vers...