This UCLA RL-LLM Bootcamp is an advanced course that connects reinforcement learning (RL) with large language models (LLMs). It provides a strong theoretical and practical foundation for understanding how modern AI systems learn through feedback, optimization, and decision-making processes.
You will begin with the fundamentals of Markov Decision Processes (MDPs), imitation learning, and value iteration. These concepts form the backbone of reinforcement learning and help explain how agents make decisions in uncertain environments.
Next, the course introduces deep policy evaluation methods, followed by advanced policy gradient techniques such as A3C. These methods are essential for training AI agents that can learn complex behaviors through trial and error.
A major section of the course focuses on PPO (Proximal Policy Optimization) and GRPO methods, which are widely used in modern reinforcement learning systems and LLM alignment training. You will also explore how these techniques are applied in real-world AI systems.
The bootcamp further covers AlphaGo, test-time compute strategies, and expert iteration, showing how reinforcement learning has been used to achieve superhuman performance in complex tasks.
Finally, the course introduces NLP foundations, language modeling, and RNNs, connecting classical sequence modeling with modern LLM ap
