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Direct Preference Optimization (DPO) is widely utilized in the Reinforcement Learning from Human Feedback (RLHF) phase to align Large Language Models (LLMs) with human preferences, thereby enhancing both their harmlessness and efficacy.
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Refined direct preference optimization with synthetic data for behavioral alignment of llms
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Eliminating biased length reliance of direct preference optimization via down-sampled kl divergence
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Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation
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Wpo: Enhancing rlhf with weighted preference optimization
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