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Reinforcement Learning from Human Feedback (RLHF)

Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.

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4 hours13 videos38 exercises

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Course Description

Combine the efficiency of Generative AI with the understanding of human expertise in this course on Reinforcement Learning from Human Feedback. You’ll learn how to make GenAI models truly reflect human values and preferences while getting hands-on experience with LLMs. You’ll also navigate the complexities of reward models and learn how to build upon LLMs to produce AI that not only learns but also adapts to real-world scenarios.
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  1. 1

    Foundational Concepts

    Free

    This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help 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.

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    Introduction to RLHF
    50 xp
    Text generation with RLHF
    100 xp
    Classifying generated text for RLHF
    100 xp
    RL vs. RLHF
    50 xp
    Exploring pre-trained LLMs
    50 xp
    Tokenize a text dataset
    100 xp
    Fine-tuning for review classification
    100 xp
    Preparing data for RLHF
    50 xp
    Preparing the preference dataset
    100 xp
    Extracting prompts
    50 xp
  2. 2

    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.

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    Methods for high-quality feedback gathering
    50 xp
    Understanding comparison and rating in RLHF
    100 xp
    Comparing slogans for a gym campaign
    100 xp
    Measuring feedback quality and relevance
    50 xp
    Low confidence
    100 xp
    K-means for feedback clustering
    100 xp
    Active learning
    50 xp
    Implementing an active learning pipeline
    100 xp
    Active learning loop
    100 xp
  3. 3

    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.

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    Reward models explored
    50 xp
    Initializing the reward
    100 xp
    Setting up the reward trainer
    100 xp
    Training with PPO
    50 xp
    Initialize the PPO trainer
    100 xp
    PPO fine-tuning
    100 xp
    Efficient fine-tuning in RLHF
    50 xp
    Prepare for 8-bit Training
    100 xp
    Train with LoRA
    100 xp
  4. 4

    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.

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    Model metrics and adjustments
    50 xp
    Mitigating negative KL divergence
    100 xp
    Checking the reward model
    50 xp
    Incorporating diverse feedback sources
    50 xp
    Majority voting on multiple data sources
    100 xp
    Unreliable data source identification
    100 xp
    Evaluating RLHF models
    50 xp
    Interpreting curves
    50 xp
    Evaluating RLHF with metrics
    50 xp
    Wrapping up your RLHF journey
    50 xp
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collaborators

Collaborator's avatar
Francesca Donadoni

prerequisites

Deep Reinforcement Learning in Python
Mina Parham HeadshotMina Parham

AI Engineer, Chubb

Mina Parham is currently working at Chubb as an AI Engineer with a strong background in LLMs, NLP, and RL. She is passionate about applying LLMs across various domains and focuses on advancing AI systems through alignment tuning techniques.
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