RLHF: Reinforcement Learning from Human Feedback
Chapter 1: Foundations of RLHF for Language Model Alignment
The AI Alignment Problem in LLMs
Limitations of Supervised Fine-Tuning
Reinforcement Learning Principles Refresher
Introduction to the RLHF Process
Setting Up the Development Environment
Chapter 2: Supervised Fine-Tuning (SFT) Phase
Role of SFT in the RLHF Pipeline
Curating High-Quality SFT Datasets
SFT Implementation Details
Evaluating SFT Model Performance
Hands-on Practical: SFT Execution
Chapter 3: Reward Modeling from Human Preferences
Concept of Learning from Preferences
Human Preference Data Collection
Preference Dataset Formats and Structures
Reward Model Architectures
Training Objectives for Reward Models
Calibration of Reward Models
Potential Issues in Reward Modeling
Hands-on Practical: Training a Reward Model
Chapter 4: RL Fine-Tuning with Proximal Policy Optimization (PPO)
PPO Algorithm for RLHF Context
Policy and Value Network Implementation
The Role of the KL Divergence Penalty
Calculating Advantages and Returns
PPO Hyperparameter Tuning for LLMs
Common PPO Implementation Libraries (TRL)
Troubleshooting PPO Training Instability
Practice: Implementing the PPO Update Step
Chapter 5: Integrating the Full RLHF Pipeline
Workflow Orchestration
Model Loading and Initialization
Generating Responses with the Policy Model
Scoring Responses with the Reward Model
Synchronizing Models During Training
Code Structure for an End-to-End RLHF System
Hands-on Practical: Running a Simplified RLHF Loop
Chapter 6: Advanced RLHF Techniques and Alternatives
Direct Preference Optimization (DPO)
Reinforcement Learning from AI Feedback (RLAIF)
Improving Sample Efficiency in RLHF
Addressing Reward Hacking Explicitly
Multi-Objective Reward Models
Contextual and Conditional RLHF
Practice: Comparing PPO and DPO Concepts
Chapter 7: Evaluation, Analysis, and Deployment
Metrics for Evaluating Aligned Models
Human Evaluation Protocols
Automated Evaluation Suites
Analyzing Policy Shift During RL Tuning
Red Teaming and Safety Testing
Computational Costs and Scalability
Deployment Considerations for RLHF Models
Hands-on Practical: Analyzing RLHF Run Logs
Training Objectives for Reward Models
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- Learning to summarize with human feedback, Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2009.01325 - An earlier work from OpenAI demonstrating the use of human feedback for training reward models, specifically for summarization tasks, employing a similar pairwise preference learning objective.
- Rank analysis of incomplete block designs. I. The method of paired comparisons, Ralph Allan Bradley and Milton E. Terry, 1952 Biometrika, Vol. 39 (Biometrika Trust) DOI: 10.1093/biomet/39.3-4.324 - The foundational paper introducing the Bradley-Terry model, which provides the probabilistic framework for pairwise comparisons and serves as the theoretical inspiration for the reward model's loss function.
- Deep Reinforcement Learning from Human Preferences, Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei, 2017 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.1706.03741 - A seminal work that proposed using human feedback, specifically pairwise comparisons, to train reward models for general deep reinforcement learning agents, predating its widespread application to language models.