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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

RLHF: Reinforcement Learning from Human Feedback

Prerequisites: Advanced ML & DL knowledge

Level:

Advanced

What You'll Learn

RLHF Pipeline Implementation
Implement the complete three-stage RLHF pipeline: Supervised Fine-Tuning (SFT), Reward Model (RM) training, and RL optimization.
Reward Modeling
Design, train, and evaluate reward models based on human preference data, including understanding data collection and annotation.
PPO for RLHF
Apply and configure Proximal Policy Optimization (PPO) specifically for fine-tuning large language models within the RLHF framework, including managing the KL divergence constraint.
Advanced RLHF Concepts
Analyze and apply advanced techniques such as Direct Preference Optimization (DPO), reward model calibration, and strategies for improving training stability.
Data Handling
Manage human preference datasets, understand data quality implications, and implement efficient data processing for RLHF.
Evaluation Methods
Evaluate RLHF-tuned models using both automated metrics and human evaluation protocols, focusing on alignment aspects.

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