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
Training the Preference Model
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- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, Jared Kaplan, 2022 arXiv preprint arXiv:2204.05862 DOI: 10.48550/arXiv.2204.05862 - This paper details the Reinforcement Learning from Human Feedback (RLHF) framework, including the architecture, loss function, and training methodology for reward models (preference models) based on human preferences.
- Constitutional AI: Harmlessness from AI Feedback, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, Jack Clark, 2022 arXiv preprint arXiv:2209.07858 DOI: 10.48550/arXiv.2209.07858 - This work describes how an AI feedback mechanism, guided by a 'constitution,' can generate the preference data that is then used to train the preference model, forming the basis of RLAIF.
- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 arXiv preprint arXiv:2106.09685 DOI: 10.48550/arXiv.2106.09685 - Introduces Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning technique that significantly reduces the number of trainable parameters, which is highly relevant for adapting large language models as preference models.
- Learning to Rank using Gradient Descent, Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Amit Lazier, Matt Deeds, Nicole Holman, Ding Zhou, 2005 Proceedings of the 22nd International Conference on Machine Learning (ICML) (ACM) DOI: 10.1145/1102351.1102432 - This foundational paper introduces RankNet, which employs a pairwise ranking loss function that is mathematically similar to the Bradley-Terry model-inspired objective used for training preference models.