Towards Reliable, Uncertainty-Aware Alignment

• URL: http://arxiv.org/abs/2507.15906v1
• Date: Mon, 21 Jul 2025 12:51:29 GMT
• Title: Towards Reliable, Uncertainty-Aware Alignment
• Abstract summary: We study the variability of reward model training on open-source benchmarks.<n>We propose a variance-aware policy optimization framework for preference-based alignment.
• Score: 12.63619480522393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alignment of large language models (LLMs) typically involves training a reward model on preference data, followed by policy optimization with respect to the reward model. However, optimizing policies with respect to a single reward model estimate can render it vulnerable to inaccuracies in the reward model. We empirically study the variability of reward model training on open-source benchmarks. We observe that independently trained reward models on the same preference dataset can exhibit substantial disagreement, highlighting the instability of current alignment strategies. Employing a theoretical model, we demonstrate that variability in reward model estimation can cause overfitting, leading to the risk of performance degradation. To mitigate this risk, we propose a variance-aware policy optimization framework for preference-based alignment. The key ingredient of the framework is a new policy regularizer that incorporates reward model variance estimates. We show that variance-aware policy optimization provably reduces the risk of outputting a worse policy than the default. Experiments across diverse LLM and reward model configurations confirm that our approach yields more stable and robust alignment than the standard (variance-unaware) pipeline.

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