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
From RLHF to RLAIF: Motivation and Differences
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- Deep Reinforcement Learning from Human Preferences, Paul Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 (Curran Associates, Inc.) DOI: 10.55917/cb47-601e - This paper introduces the method of training a reward function from human feedback on trajectory segments, laying the groundwork for Reinforcement Learning from Human Feedback (RLHF) techniques.
- Training language models to follow instructions with human feedback, Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, Ryan Lowe, 2022 Advances in Neural Information Processing Systems, Vol. 35 (Curran Associates, Inc.) DOI: 10.48550/arXiv.2203.02155 - This paper details the InstructGPT model, demonstrating how Reinforcement Learning from Human Feedback (RLHF) can effectively align large language models with user instructions and preferences, highlighting the process and its human data requirements.
- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandhini Agarwal, Andy Jones, Anna Chen, Cameron McKinnon, Carole-Anne Razavi, Edouard Charette, Jackson Kernion, Jeremiah Kaplan, Kristen Hilton, Lee Sharkey, Maciej Korbak, Martin Wattenberg, Micah Rosenkranz, Morningstar Anguige, Nikhil Chelluri, Nicholas Schiefer, Nicole Sanchez, Sam Bowman, Scott McGhew, Shauna Gordon-Nunez, Stephen Casper, Stephen Marcus, Tom Brown, Tamera Lanham, Zac Hatfield-Dodds, Ben Mann, Amanda Askell, Jack Clark, Sam McCandlish, Dario Amodei, and Jared Kaplan, 2023 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2212.08073 - This foundational paper introduces Constitutional AI, a method that uses AI feedback (RLAIF) to train models to be harmless by aligning them with a set of principles, directly addressing the scalability and cost challenges of human feedback.