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Course Outline

Introduction to Reinforcement Learning from Human Feedback (RLHF)

  • Understanding RLHF and its significance.
  • Comparing RLHF with supervised fine-tuning methods.
  • Applications of RLHF in modern AI systems.

Reward Modeling with Human Feedback

  • Collecting and structuring human feedback.
  • Building and training reward models.
  • Evaluating the effectiveness of reward models.

Training with Proximal Policy Optimization (PPO)

  • Overview of PPO algorithms for RLHF.
  • Implementing PPO alongside reward models.
  • Iteratively and safely fine-tuning models.

Practical Fine-Tuning of Language Models

  • Preparing datasets for RLHF workflows.
  • Hands-on fine-tuning of a small LLM using RLHF.
  • Challenges and mitigation strategies.

Scaling RLHF to Production Systems

  • Infrastructure and compute considerations.
  • Quality assurance and continuous feedback loops.
  • Best practices for deployment and maintenance.

Ethical Considerations and Bias Mitigation

  • Addressing ethical risks in human feedback.
  • Strategies for bias detection and correction.
  • Ensuring alignment and safe outputs.

Case Studies and Real-World Examples

  • Case study: Fine-tuning ChatGPT with RLHF.
  • Other successful RLHF deployments.
  • Lessons learned and industry insights.

Summary and Next Steps

Requirements

  • A solid understanding of supervised and reinforcement learning fundamentals.
  • Practical experience in model fine-tuning and neural network architectures.
  • Familiarity with Python programming and deep learning frameworks (e.g., TensorFlow, PyTorch).

Target Audience

  • Machine learning engineers.
  • AI researchers.
 14 Hours

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  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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