Preference-based alignment methods such as Direct Preference Optimization (DPO) and Binary Classifier Optimization (BCO) offer efficient alternatives to reinforcement learning from human feedback (RLHF). However, they often treat comparisons as independent labels and do not explicitly model uncertainty, which can lead to miscalibration and reduced robustness under noisy or heterogeneous feedback. We introduce Belief Propagation for Large Language Model Alignment (BP–LLM), a probabilistic framework that views binary feedback as noisy observations of latent reward margins under a policy-induced Gaussian prior. Using the Jaakkola–Jordan variational bound, BP–LLM yields closed-form Gaussian message updates and performs stable belief propagation between a classifier-side inference module and the policy. Exchanging extrinsic messages enables both modules to refine beliefs without double counting and recovers BCO and DPO as special cases. We evaluate BP–LLM in two regimes. In an inference-only setting with frozen LLM weights, BP–LLM consistently improves label-free test-time win rate over BCO and DPO across UltraFeedback, Capybara, and HelpSteer2 for open-weight Llama and Qwen models. In a training-time setting with parameter-efficient LoRA updates, BP–LLM also outperforms Cal-DPO with higher win rates. Overall, BP–LLM is most beneficial under noisy/heterogeneous (or unary) feedback, where posterior refinement and extrinsic shaping provide more reliable signals than hard labels, while remaining lightweight and scalable.1