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DPO
Direct Preference Optimization. A simpler alternative to RLHF for tuning model behavior.
DPO (Direct Preference Optimization) is a fine-tuning technique that trains a model to prefer one output over another using paired preference data (chosen vs. rejected). Unlike full RLHF it does not need a separate reward model or reinforcement-learning loop. In practice DPO is easier to set up than RLHF and almost as effective for most preference-tuning use cases.
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