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Direct Preference Optimization (DPO) is a novel fine-tuning technique that has become popular due to its simplicity and ease of implementation. It has emerged as a direct alternative to reinforcement learning from human feedback (RLHF) for large language models. DPO uses LLM as a reward model to optimize the policy, leveraging human preference data to identify which responses are preferred and which are not.

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