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.

Introduction to RLHF

Text generation with RLHF

Classifying generated text for RLHF

RL vs. RLHF

Exploring pre-trained LLMs

Tokenize a text dataset

Fine-tuning for review classification

Preparing data for RLHF

Preparing the preference dataset

Extracting prompts

Foundational Concepts

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. 

Methods for high-quality feedback gathering

Understanding comparison and rating in RLHF

Comparing slogans for a gym campaign

Measuring feedback quality and relevance

Low confidence

K-means for feedback clustering

Active learning

Implementing an active learning pipeline

Active learning loop

Gathering 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. 

Reward models explored

Initializing the reward

Setting up the reward trainer

Training with PPO

Initialize the PPO trainer

PPO fine-tuning

Efficient fine-tuning in RLHF

Prepare for 8-bit Training

Train with LoRA

 Tuning Models with Human Feedback

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.

Modelmetingen en bijsturing

Negatieve KL-divergentie beperken

Het rewardmodel controleren

Diverse feedbackbronnen integreren

Meerderheidsstemming over meerdere databronnen

Onbetrouwbare gegevensbron identificeren

RLHF-modellen evalueren

Curves interpreteren

RLHF evalueren met metriek

Afronding van je RLHF-reis

Model Evaluation

Combineer de efficiëntie van Generatieve AI met de inzichten van menselijke expertise in deze cursus over Reinforcement Learning from Human Feedback. Je leert GenAI-modellen echt laten aansluiten bij menselijke waarden en voorkeuren, terwijl je praktisch aan de slag gaat met LLM’s. Ook navigeer je door de complexiteit van beloningsmodellen en leer je hoe je voortbouwt op LLM’s om AI te maken die niet alleen leert, maar zich ook aanpast aan situaties uit de echte wereld.

Deep Reinforcement Learning in Python

Leer hoe je GenAI-modellen menselijke waarden laat weerspiegelen en ervaring opdoet met geavanceerde LLMs.

Reinforcement Learning from Human Feedback (RLHF)

Leer hoe je GenAI-modellen echt menselijke waarden kunt laten weerspiegelen en doe tegelijkertijd praktische ervaring op met geavanceerde LLM's.

Grote taalmodellen ontwikkelen

Versterkend leren in Python

Negatieve KL-divergentie beperken

Reinforcement Learning from Human Feedback (RLHF)

Oefeninstructies

Praktische interactieve oefening