Triple Preference Optimization: Achieving Better Alignment with Less
Data in a Single Step Optimization
Triple Preference Optimization: Achieving Better Alignment with Less
Data in a Single Step Optimization
Large Language Models (LLMs) perform well across diverse tasks, but aligning them with human demonstrations is challenging. Recently, Reinforcement Learning (RL)-free methods like Direct Preference Optimization (DPO) have emerged, offering improved stability and scalability while retaining competitive performance relative to RL-based methods. However, while RL-free methods deliver satisfactory performance, they …