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
Computational Costs and Scalability
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- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, 2021 arXiv preprint arXiv:2106.09685 DOI: 10.48550/arXiv.2106.09685 - Introduces Low-Rank Adaptation (LoRA), a prominent parameter-efficient fine-tuning technique that significantly reduces memory and computational costs for training large language models.
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, Samyam Rajbhandari, Cong Li, Zhun Liu, Kshitij Kumar, and Yuxiong He, 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (IEEE) DOI: 10.1109/SC20-501.2020.00018 - Presents ZeRO (Zero Redundancy Optimizer), a family of memory optimization technologies essential for distributed training of massive deep learning models by sharding model states across devices.
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model, Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn, 2023 arXiv preprint arXiv:2305.18290 DOI: 10.48550/arXiv.2305.18290 - Introduces Direct Preference Optimization (DPO), an alternative algorithm to PPO for fine-tuning language models with human preferences, which simplifies the training process by removing the explicit reward model.