Prerequisites: Deep expertise in Large Language Models (architecture, training, fine-tuning), Reinforcement Learning (algorithms like PPO), and Natural Language Processing required. Proficiency in Python and standard ML frameworks (PyTorch/TensorFlow) assumed.

Level: Expert

What You'll Learn

• Constitutional AI Principles

  Comprehend the advanced theoretical foundations and mechanisms of Constitutional AI for guiding LLM behavior.

• RLAIF Implementation

  Implement RLAIF pipelines, including AI-generated preference modeling and reinforcement learning updates.

• CAI System Design

  Design and implement the supervised learning phase of CAI, generating AI critiques and refinements based on a constitution.

• Integrated Alignment Strategies

  Combine CAI and RLAIF techniques for enhanced LLM alignment, addressing potential conflicts and synergies.

• Advanced Evaluation Methodologies

  Apply rigorous evaluation techniques specifically designed for CAI and RLAIF-aligned models, including robustness testing.

• Optimization and Scalability

  Analyze and apply optimization techniques for scaling CAI and RLAIF training pipelines efficiently.