
Reinforcement Learning LLM: Practical Methods to Align, Fine-Tune, and Control Large Language Models
Jason Koller
Premium
Opening Credits
1/14/2026
Chapter 1: From Supervised Language Modeling to Reinforcement Learning
1/14/2026
Chapter 2: Reinforcement Learning Essentials Reframed for Language Models
1/14/2026
Chapter 3: Algorithms in Practice Policy Gradients, Proximal Methods, and Bandits
1/14/2026
Chapter 4: Designing Rewards for Language Models Preferences, Metrics, and Tradeoffs
1/14/2026
Chapter 5: Collecting Preference Data and Training Reward Models
1/14/2026
Chapter 6: Alignment via Human and AI Feedback Safety Constraints and Guardrails
1/14/2026
Chapter 7: Building the RL Fine Tuning Pipeline from Warm Start to Online Updates
1/14/2026
Chapter 8: Scaling and Stabilizing RL for Large Language Models
1/14/2026
Chapter 9: Evaluating Helpful and Harmless Behavior Offline and Online RL Settings
1/14/2026
Chapter 10: Beyond RLHF Controllable Generation, Tools, and Open Challenges
1/14/2026
Closing Credits
1/14/2026