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
Efficient Implementation of Feedback Generation
Was this section helpful?
- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan, 2022 arXiv preprint arXiv:2212.08073 DOI: 10.48550/arXiv.2212.08073 - Presents the Constitutional AI framework, which involves generating AI critiques and revisions, a core topic of this section.
- Deep reinforcement learning from human preferences, Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei, 2017 arXiv preprint arXiv:1706.03741 DOI: 10.48550/arXiv.1706.03741 - Foundational work on training reinforcement learning agents using human feedback, a precursor to RLAIF and essential for understanding preference labeling.
- QLoRA: Efficient Finetuning of Quantized LLMs, Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 arXiv preprint arXiv:2305.14314 DOI: 10.48550/arXiv.2305.14314 - Introduces an efficient fine-tuning approach for quantized large language models, relevant for optimizing feedback models through quantization.
- Distilling the knowledge in a neural network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015 arXiv preprint arXiv:1503.02531 DOI: 10.48550/arXiv.1503.02531 - A foundational paper introducing the concept of model distillation, where a smaller 'student' model is trained to mimic a larger 'teacher' model.