RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models

• URL: http://arxiv.org/abs/2602.24040v1
• Date: Fri, 27 Feb 2026 14:15:57 GMT
• Title: RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
• Authors: Daniel Yang, Samuel Stante, Florian Redhardt, Lena Libon, Parnian Kassraie, Ido Hakimi, Barna Pásztor, Andreas Krause,
• Abstract summary: Reward models are central to aligning large language models with human preferences.<n>Uncertainty in reward models arising from limited human feedback can reduce the costs of human annotation.<n>This work introduces a unified framework, RewardUQ, to systematically evaluate uncertainty quantification for reward models.
• Score: 31.643743375369677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward estimates that overlook the epistemic uncertainty in reward models arising from limited human feedback. Recent work suggests that quantifying this uncertainty can reduce the costs of human annotation via uncertainty-guided active learning and mitigate reward overoptimization in LLM post-training. However, uncertainty-aware reward models have so far been adopted without thorough comparison, leaving them poorly understood. This work introduces a unified framework, RewardUQ, to systematically evaluate uncertainty quantification for reward models. We compare common methods along standard metrics measuring accuracy and calibration, and we propose a new ranking strategy incorporating both dimensions for a simplified comparison. Our experimental results suggest that model size and initialization have the most meaningful impact on performance, and most prior work could have benefited from alternative design choices. To foster the development and evaluation of new methods and aid the deployment in downstream applications, we release our open-source framework as a Python package. Our code is available at https://github.com/lasgroup/rewarduq.

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