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An alignment method that trains a model directly on human preference pairs with a simple classification loss, skipping the separate reward model used in RLHF.
Rafailov et al. (2023) showed that the RLHF objective can be reparameterised so the language model is, in effect, its own reward model. Direct Preference Optimization then trains on preferred-versus-rejected response pairs with a simple classification-style loss — achieving alignment comparable to RLHF without the complexity and instability of separate reward modelling and reinforcement learning.
DPO has become a popular, lighter-weight alternative to full RLHF pipelines.
Primary, peer-reviewed and archival sources for this definition.
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