The RLHF pipeline comprises supervised fine-tuning, preference sampling, and reward learning, followed by reinforcement learning optimization, enhancing model effectiveness in decision making.
Our analysis of Direct Preference Optimization (DPO) reveals that it allows for significant improvements in performance metrics, making it a robust alternative to traditional training methods.
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