When Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF

• URL: http://arxiv.org/abs/2512.00709v1
• Date: Sun, 30 Nov 2025 03:16:20 GMT
• Title: When Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF
• Authors: Yifan Xu, Xichen Ye, Yifan Chen, Qiaosheng Zhang,
• Abstract summary: We introduce a Flipping-Aware Direct Preference Optimization (FA-DPO) algorithm tailored to preference flipping from a reinforcement learning perspective.<n>By leveraging features relevant to preference annotation, we capture uncertainty in judgments and model preference flipping patterns.<n>In our experiments, we investigate the instance-dependent preference flipping model under multiple circumstances.
• Score: 14.663977441172115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quality of datasets plays an important role in large language model (LLM) alignment. In collecting human feedback, however, preference flipping is ubiquitous and causes corruption in data annotation; the issue necessitates the alignment algorithms with improved robustness against potential flipped pairs. To this end, this paper introduces a Flipping-Aware Direct Preference Optimization (FA-DPO) algorithm tailored to preference flipping from a reinforcement learning with human feedback (RLHF) perspective. We dissect the inherent human intention model and the preference flipping mechanism introduced by external factors as two distinct stages; in the latter, we introduce an instance-dependent flipping probability on the basis of the Bradley-Terry (BT) model. Further, by leveraging features relevant to preference annotation, we capture uncertainty in judgments and model preference flipping patterns. In practice, we design a simple yet efficient iterative optimization algorithm compatible with the original RLHF and DPO algorithms. In our experiments, we investigate the instance-dependent preference flipping model under multiple circumstances for evaluation of our proposed method, as well as other baseline methods.

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