Abstract Direct Preference Optimization (DPO) trains a language model using human preference data, bypassing the explicit reward modeling phase of Reinforcement Learning from Human Feedback (RLHF). By iterating over sentence pairs in a preference dataset, DPO enhances generation quality by increasing the likelihood of producing preferred sentences over less favored ones. Preference datasets, typically labeled with votes or scores, provide valuable insights into whether a sentence pair exhibits a clear preference or remains controversial. However, existing methods do not fully utilize this information. In this article, we propose a technique that leverages user voting data to better align language models with diverse subjective preferences. We use the Bayesian Minimum Mean Square Error (Bayesian MMSE) estimator to model the probability that one generation is preferred over another. Using this estimated probability as a target, we introduce the Vote-based Preference Optimization (VPO) framework, which incorporates the number of votes on both sides to distinguish between controversial and clearly preferred generation pairs. Furthermore, we demonstrate that previous algorithms, such as DPO and Identity Preference Optimization (IPO), can be extended using the proposed framework, termed VDPO and VIPO. Our experiments demonstrate that these proposed algorithms outperform various existing methods, including their base algorithms. Additionally, our framework can be applied to reward modeling, demonstrating that our approach is compatible with the broader RLHF pipeline.