Skip to content

5-B RLHF & Policy Tuning

Coming Soon

This lesson is currently under development. We're working on comprehensive content covering:

- Direct Preference Optimization (DPO)
- Reward model design and training
- Federated learning loops
- Policy gradient methods
- Continuous learning and adaptation

**Want to contribute?** Check out our [GitHub repository](https://github.com/karthikkpro/ai-agent-engineer-course) or join our discussions!

Learning Objectives

  • Master Direct Preference Optimization (DPO) techniques
  • Design and train effective reward models
  • Implement federated learning loops
  • Apply policy gradient methods for agent tuning

Key Topics

  • Direct Preference Optimization: DPO implementation and optimization
  • Reward Models: Design, training, and validation
  • Federated Learning: Distributed training and privacy
  • Policy Gradients: Reinforcement learning for agents
  • Continuous Learning: Adaptive and evolving systems

This lesson will be available soon. Stay tuned for updates!