An advanced course on Reinforcement Learning from Human Feedback (RLHF) for aligning large language models. This material covers the theoretical underpinnings and practical implementation details of RLHF, including reward modeling, Proximal Policy Optimization (PPO) fine-tuning, and data collection strategies. Suitable for engineers and researchers with a strong background in machine learning and deep learning.
Prerequisites Advanced ML & DL knowledge
Level:
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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