A Guide to Reinforcement Learning Post-Training for LLMs: PPO, DPO, GRPO, and Beyond

Reinforcement learning post-training for large language models (LLMs) involves various techniques to optimize model performance, such as Proximal Policy Optimization (PPO), Direct Preference Optimization (DPO), and Generalized Reward Policy Optimization (GRPO). These methods use reinforcement learning principles to fine-tune LLMs by improving their response generation through feedback based on human preferences or other reward signals. PPO enhances model stability with mechanisms like Generalized Advantage Estimation and clipped updates, whereas DPO directly leverages preference data to refine models without a separate reward model. GRPO addresses memory-intensive challenges by skipping certain network components and utilizing group-based advantage calculations. These approaches aim to maximize expected returns by adjusting model parameters, utilizing KL divergence penalties to prevent excessive deviation from pre-trained reference models. The text also highlights the importance of understanding different KL divergence types and estimation pitfalls to ensure effective training and addresses memory and computational efficiency in developing these advanced LLMs.