Wrapping up your RLHF journey

1. Wrapping up your RLHF journey

Congratulations on completing this course on Reinforcement Learning with Human Feedback! Throughout the journey, you've gained foundational knowledge and hands-on experience in RLHF models.

2. Starting the journey with foundational concepts

In Chapter 1, you explored the core concepts of RLHF, compared it to traditional reinforcement learning, and looked at some pre-trained language models. You also learned how to prepare datasets for RLHF by extracting prompts and organizing feedback.

3. Gathering high-quality feedback

In Chapter 2 you focused on methods for gathering high-quality human feedback, ensuring data relevance, and implementing human-in-the-loop architectures, emphasizing the critical role humans play in refining these systems.

4. Reward models and human feedback in the loop

In Chapter 3, you explored reward models further, learning to build, train, and tune them. You also implemented the PPO training loop, fully integrating human feedback into the process.

5. Metrics and evaluation

Finally, in Chapter 4, you explored metrics and evaluation, focusing on optimizing model behavior through metrics and adjustments, and evaluating models using both automated and human-centered techniques.

6. Congratulations!

With these skills, you're now ready to build more human-centered AI systems. We encourage you to keep experimenting and advancing your RLHF techniques. Thank you for joining us on this journey!

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This exercise is part of the course

Reinforcement Learning from Human Feedback (RLHF)

AdvancedSkill Level

4.8+

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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.

Exercise 1: Introduction to RLHF Exercise 2: Text generation with RLHF Exercise 3: Classifying generated text for RLHF Exercise 4: RL vs. RLHF Exercise 5: Exploring pre-trained LLMs Exercise 6: Tokenize a text dataset Exercise 7: Fine-tuning for review classification Exercise 8: Preparing data for RLHF Exercise 9: Preparing the preference dataset Exercise 10: Extracting prompts

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.

Exercise 1: Methods for high-quality feedback gathering Exercise 2: Understanding comparison and rating in RLHF Exercise 3: Comparing slogans for a gym campaign Exercise 4: Measuring feedback quality and relevance Exercise 5: Low confidence Exercise 6: K-means for feedback clustering Exercise 7: Active learning Exercise 8: Implementing an active learning pipeline Exercise 9: Active learning loop

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.

Exercise 1: Reward models explored Exercise 2: Initializing the reward Exercise 3: Setting up the reward trainer Exercise 4: Training with PPO Exercise 5: Initialize the PPO trainer Exercise 6: PPO fine-tuning Exercise 7: Efficient fine-tuning in RLHF Exercise 8: Prepare for 8-bit Training Exercise 9: Train with LoRA

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.

Exercise 1: Model metrics and adjustments Exercise 2: Mitigating negative KL divergence Exercise 3: Checking the reward model Exercise 4: Incorporating diverse feedback sources Exercise 5: Majority voting on multiple data sources Exercise 6: Unreliable data source identification Exercise 7: Evaluating RLHF models Exercise 8: Interpreting curves Exercise 9: Evaluating RLHF with metrics Exercise 10: Wrapping up your RLHF journey

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