A Deep Dive into Policy Optimization Algorithms & Frameworks for Model Alignment

Reinforcement Learning (RL) is a complex area of machine learning focusing on training intelligent agents to optimize their actions for maximum cumulative rewards, with recent advancements highlighting policy optimization algorithms and frameworks. Unlike supervised learning, RL involves dynamically generated training data, which can cause instability and sensitivity to hyperparameters. Notable algorithms such as Proximal Policy Optimization (PPO) and Reinforcement Learning with Human Feedback (RLHF) have made significant strides, offering methods like Direct Preference Optimization (DPO) and innovations like Self-Play Fine Tuning (SPIN), addressing computational challenges and improving model efficiency. These developments, alongside frameworks like KTO, RSO, and SRLM, showcase the field's evolution towards more sophisticated, stable, and efficient models, continually enhancing RL's capacity to handle complex tasks and optimize language models. As the domain progresses, it holds promise for further breakthroughs in optimizing policy objectives and refining machine learning methodologies.