Advanced LLM Alignment: Constitutional AI and RLAIF
Chapter 1: The Scalable Alignment Problem
Limitations of Supervised Fine-Tuning for Alignment
Challenges in Reinforcement Learning from Human Feedback (RLHF)
Defining Scalable Oversight
The Need for AI Feedback Mechanisms
Theoretical Frameworks for AI-Assisted Alignment
Chapter 2: Constitutional AI: Theoretical Deep Dive
Core Principles of Constitutional AI
Designing Effective Constitutions
The Supervised Learning Phase (Critique and Revision)
Mathematical Formulation of CAI Feedback
Relationship to Instruction Following
Limitations and Critiques of the CAI Framework
Chapter 3: Implementing Constitutional AI Systems
Setting up the Constitution Document
Generating Initial Responses
Implementing the AI Critiquer Model
Implementing the AI Revision Model
Constructing the Supervised Fine-Tuning Dataset
Fine-Tuning the LLM with CAI Data
Debugging and Iterating on the CAI Process
Hands-on Practical: Building a Simple CAI Critique Step
Chapter 4: Reinforcement Learning from AI Feedback (RLAIF)
From RLHF to RLAIF: Motivation and Differences
AI Preference Modeling Techniques
Generating AI Preference Labels
Designing Reward Functions from AI Preferences
Reinforcement Learning Algorithms for RLAIF (Advanced PPO)
Addressing Stability and Convergence in RLAIF
Theoretical Guarantees and Limitations of RLAIF
Chapter 5: Advanced RLAIF Implementation Details
Building the AI Preference Labeler
Preference Data Collection and Management
Training the Preference Model
Implementing the PPO Loop for RLAIF
Hyperparameter Tuning for RLAIF Systems
Scaling RLAIF Pipelines
Common Failure Modes and Debugging Strategies
Practice: Training a Basic AI Preference Model
Chapter 6: Integrating CAI and RLAIF
Synergistic Opportunities: CAI Guiding RLAIF
Using CAI Outputs as Input for RLAIF
Sequential vs. Joint Training Pipelines
Handling Conflicts Between Constitution and AI Preferences
Architectural Considerations for Combined Systems
Comparative Performance Analysis
Chapter 7: Advanced Evaluation of Aligned Models
Standard Benchmarks: Alignment-Specific Metrics
Red Teaming Strategies for CAI/RLAIF Models
Robustness Testing Against Adversarial Inputs
Analyzing Failure Modes Specific to AI Feedback
Statistical Significance in Alignment Evaluation
Qualitative Analysis of Model Behavior
Hands-on Practical: Designing a Red Teaming Test Suite
Chapter 8: Optimization and Scalability Considerations
Computational Costs of CAI and RLAIF
Efficient Implementation of Feedback Generation
Optimizing the RL Training Loop (PPO Efficiency)
Distributed Training Strategies
Model Distillation for Aligned Models
Quantization and Pruning Considerations
Resource Management and Infrastructure Planning
Defining Scalable Oversight
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- Training language models to follow instructions with human feedback, Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Karthik Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, S.K. Sutskever, Amanda Askell, Sarita Char, Janelle Shane, Brian Mcmahan, Noah Fiedel, Paul Christiano, Geoff Irving, Kyle Scott, 2022 arXiv preprint arXiv:2203.02155 DOI: 10.48550/arXiv.2203.02155 - Introduces Reinforcement Learning from Human Feedback (RLHF), which this section identifies as having scaling challenges due to its reliance on continuous human preference labeling.
- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Roslyn Campbell, Anna Chen, Dawn Drain, Deep Ganguli, Andy Jones, Nicholas Joseph, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tom Henighan, Brian Hutchinson, Rita Johnston, Abhishek Karkhanis, Jeremy Kim, Carol Chen, Kristóf T. Garamvölgyi, Sam McCandlish, Chris Olah, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Samuel R. Bowman, Kevin Scott, Shauna Gordon-McKeon, Lauren Hume, Michael Johnston, Ben Mann, Amanda Ngo, Arvind Neelakantan, Long Ouyang, Catherine Perez, Nicholas Schiefer, Justin Shlegeris, Stephanie Sclafani, Gabe Selsky, Sam Ringer, Mike Smith, Jordan Schneider, Noah Shinn, Brooke Smyth, Stephen McAleer, Andrew Trask, Jon Uesato, Jeff Wu, Danny Wu, Steven T. Young, Evan Hubinger, 2022 arXiv preprint arXiv:2212.08073 (arXiv) DOI: 10.48550/arXiv.2212.08073 - Directly introduces Constitutional AI as a method for scalable alignment, specifically leveraging AI feedback to reduce the need for extensive human supervision.
- RLAIF: Scaling Reinforcement Learning from Human Feedback with AI-Generated Feedback, Sungdong Lee, Seonghyeok Park, Hyeonseung Lee, et al., 2023 arXiv preprint arXiv:2309.00267 - Focuses on Reinforcement Learning from AI Feedback (RLAIF), a method designed to scale alignment by using AI-generated preferences to train models, directly addressing the bottleneck of human feedback.