This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
Trained on a vast dataset comprising primarily GPT-4 generated data and supplemented with high-quality information from open datasets in the AI field, this model exhibits exceptional performance across various tasks. It introduces a novel SFT + DPO version, and for those who prefer a different approach, an SFT-only version is also made available
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