Reinforcement Learning from Human Feedback (RLHF)

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Overview

Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge approach in artificial intelligence that combines reinforcement learning with guidance from human evaluators to train models that better align with human preferences and values. Unlike traditional reinforcement learning, which relies solely on predefined reward functions, RLHF incorporates direct human input to shape and improve model behavior in complex, real-world tasks. In RLHF, humans provide feedback on model outputs, such as rankings or preferences, which are then used to train a reward model....

Syllabus

Foundational Concepts

This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.

Gathering Human Feedback

Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.

Tuning Models with Human Feedback

In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.

Model Evaluation

Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.