AllocateWhat is DPO (direct preference optimization)?
DPO (direct preference optimization) trains a model on preference pairs, a chosen answer and a rejected one, by directly adjusting the model to favor the chosen response. It reaches most of RLHF’s alignment quality without training a separate reward model or running a reinforcement-learning loop.
The simplicity is the point: DPO is a supervised-style objective over data you may already have. Every time a reviewer picks version A over version B, that is a DPO pair.
In practice teams combine methods: supervised fine-tuning teaches the task, then DPO sharpens judgment on the cases where the first model chose wrong. Both run as ordinary fine-tuning jobs on open-weight bases.
Related terms
Allocate is the cloud inference platform for companies that want to train and run their own models.