A Practical Guide to Reinforcement Learning from Human Feedback
Foundations, aligning large language models, and the evolution of preference-based methods
Created by Sandip Kulkarni
Explore how reinforcement learning from human feedback helps align AI models with human values. You'll discover practical methods for training large language models using human preferences and reward modeling. Gain the skills to build safer, more reliable AI systems that better reflect real-world needs.
Packt | Mar 2026 | 402 min
What You Will Learn
You will start by building a solid understanding of reinforcement learning fundamentals and reward modeling. Through hands-on examples, you will collect and use human feedback to optimize AI models. As you progress, you will tackle policy optimization, fine-tuning, and evaluation strategies to ensure your models are both effective and aligned with human values.
Key Features
- Develop practical skills in reward modeling and human preference data collection
- Fine-tune large language models using reinforcement learning techniques
- Address challenges like bias and scalability in real-world AI alignment
Target Audience
Designed for AI practitioners, machine learning engineers, and researchers with some experience in AI or machine learning. If you want to implement reinforcement learning from human feedback in real-world projects or deepen your understanding of AI alignment and large language models, this course will help you reach your goals.
