Reinforcement Learning from Human Feedback (RLHF)

What You'll Learn

• Gain a conceptual understanding of Reinforcement Learning from Human Feedback (RLHF) and the datasets needed for this technique.
• Fine-tune the Llama 2 model using RLHF with the open-source Google Cloud Pipeline Components Library.
• Evaluate tuned model performance against the base model using various evaluation methods.

About This Course

Large language models (LLMs) are primarily trained on human-generated text, but aligning them with human values and preferences requires additional techniques. Reinforcement Learning from Human Feedback (RLHF) is a key method for this alignment, especially for fine-tuning models to meet specific use-case preferences. This course provides a hands-on approach to RLHF, allowing you to practice tuning and evaluating an LLM.

• Explore the two main datasets in RLHF: the “preference” and “prompt” datasets.
• Use the Google Cloud Pipeline Components Library to fine-tune the Llama 2 model using RLHF techniques.
• Compare and evaluate the tuned model against the original base model using loss curves and the Side-by-Side (SxS) evaluation method.

By completing this course, you’ll understand the RLHF process and have practical skills to apply this technique for aligning LLMs with specific preferences and values.

Course Outline

• Introduction
  Introduction to RLHF and its significance in aligning LLMs with human values and preferences.
• How does RLHF work
  Overview of the RLHF training process and its practical applications.
• Datasets for RL training
  Understanding the “preference” and “prompt” datasets and their role in RLHF training.
• Tune an LLM with RLHF
  Using the Google Cloud Pipeline Components Library to fine-tune the Llama 2 model with RLHF.
• Evaluate the tuned model
  Evaluation techniques, including loss curves and Side-by-Side (SxS) comparisons, for assessing tuned model performance.
• Google Cloud Setup
  Setting up Google Cloud components and environments for RLHF training and tuning.
• Conclusion
  Summary of RLHF concepts and techniques for applying this method to specific LLM use cases.

Who Should Join?

This course is designed for individuals with intermediate Python knowledge who are interested in learning and applying Reinforcement Learning from Human Feedback to align LLMs with human values and preferences.