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
Synchronizing Models During Training
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- Proximal Policy Optimization Algorithms, John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov, 2017 arXiv preprint arXiv:1707.06347 DOI: 10.48550/arXiv.1707.06347 - Explains the core PPO algorithm, including the role of the old policy and value function in stabilizing training.
- Distributed Communication Package - torch.distributed, PyTorch Authors, 2024 (PyTorch) - Provides details on implementing distributed training in PyTorch, covering gradient aggregation and process synchronization.
- TRL - Transformers Reinforcement Learning (Documentation), Hugging Face, 2024 (Hugging Face) - Offers practical guidance and code examples for implementing RLHF pipelines, including the PPO training loop and model management.