Prerequisites: Deep understanding of Reinforcement Learning (including PPO), Large Language Models (LLMs), and proficiency in Python with ML frameworks like PyTorch or TensorFlow.

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.