Implement sophisticated alignment techniques for Large Language Models using Constitutional AI (CAI) and Reinforcement Learning from AI Feedback (RLAIF). This course covers the theoretical underpinnings, practical implementation details, advanced optimization strategies, and comparative analysis for building safer and more reliable AI systems. Suitable for experienced AI engineers and researchers.
Prerequisites Advanced LLM & RL experience.
Level:
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.