Welcome to the third set of flashcards, designed to help you build or revise your machine learning (ML) knowledge whenever needed. If last time we explored types of deep learning for you, today we look into reinforcement learning – but with a twist. The following four reinforcement learning (RL) frameworks build on the classic RL model, adapting it for more nuanced forms of feedback and interaction with AI and human agents, addressing limitations in traditional reinforcement learning. The most famous is RLHF—reinforcement learning with human feedback. But there’s more: RLAIF, RLEF, RLCF… A little head-spinning, I know.

Whether you're an adult or a kid, we hope these flashcards will help you understand what each of these acronyms means and how each of these reinforcement learning techniques works. 

Okay, let’s get the set of cards for RLHF, often called 'the secret sauce behind ChatGPT.'

Now, onto RLAIF, where human feedback is no longer needed. AI provides the feedback.

The next two approaches, RLEF and RLCF, were introduced recently (and more RL+F approaches are on the way, including hybrid models! But that’s for another time).

You are welcome to share these cards! For our Premium subscribers, we prepared a downloadable PDF-version and a collection of resources to dive deeper →

The flashcards series is a pure experiment, and it will be evolving with time and your feedback. If you want to help create them or can recommend cool tools that could assist with this – let me know at ks@turingpost.com

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