Reinforcement Learning for Large Language Models - RLHF, PPO, DPO, and GRPO

Learn reinforcement learning techniques specifically designed for training and fine-tuning large language models through this comprehensive video tutorial. Master Reinforcement Learning with Human Feedback (RLHF) to understand how to train and fine-tune Transformer models effectively. Explore Proximal Policy Optimization (PPO) methods for training large language models and discover Direct Preference Optimization (DPO) as an alternative approach to fine-tune LLMs without traditional reinforcement learning. Dive into Group Relative Policy Optimization (GRPO) to understand how DeepSeek trains reasoning models, and gain proficiency in KL Divergence to measure differences between distributions. Build foundational knowledge through a friendly introduction to deep reinforcement learning, Q-networks, and policy gradients, providing you with the theoretical and practical understanding needed to implement these advanced techniques in your own LLM projects.