The Allen Institute for AI has released the Tulu 2.5 suite, a collection of advanced AI models trained using Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). The suite includes a variety of models trained on various datasets to enhance their reward and value models. This release aims to significantly improve language model performance across several domains.
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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