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
PPO Algorithm for RLHF Context
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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 - The original paper introducing the PPO algorithm, detailing its objective clipping and KL penalty mechanisms for stable and efficient policy optimization.
- Training LMs with Human Feedback, Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. 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 Christiano, Jan Leike, Ryan Lowe, 2022 Advances in Neural Information Processing Systems (NeurIPS 2022), Vol. 35 DOI: 10.48550/arXiv.2203.02155 - A landmark paper that demonstrates using PPO for fine-tuning large language models with human preferences, forming the basis of RLHF in LLMs.
- Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, 2018 (MIT Press) - A standard textbook on reinforcement learning, providing foundational understanding of policy gradient methods, actor-critic architectures, and other core RL concepts.
- High-Dimensional Continuous Control Using Generalized Advantage Estimation, John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel, 2015 International Conference on Learning Representations (ICLR 2016) DOI: 10.48550/arXiv.1506.02438 - This paper introduces Generalized Advantage Estimation (GAE), a method to reduce variance in policy gradient estimates, which is crucial for stable PPO training.