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From Autocomplete to Assistant: RL for Beginners
New
1 students
From Autocomplete to Assistant: RL for Beginners
Learn how ChatGPT is really trained: RLHF, reward models, PPO, GRPO, DPO and reasoning models. No maths required.
Created byShashank Jain
Last updated 8/2026
English
What you'll learn
- Explain how modern AI assistants are really trained, from pretraining to RLHF, and why a model that read the internet still can't answer your question
- Describe what a reward model is and how it learns human taste from simple A/B comparisons, without anyone ever writing down a single score
- Understand reinforcement learning from zero: policy, reward, baseline and advantage, explained with no maths and no prior RL background
- Tell PPO, GRPO, RLOO and DPO apart, and explain in plain English what these algorithms actually disagree about
- Explain why verifiable rewards (RLVR) produced today's reasoning models, and how thinking for longer makes answers better
- Diagnose why AI models turn sycophantic, verbose or over-cautious, and trace each behaviour back to its real cause in the training data
- Read published benchmark scores critically, understanding contamination, format sensitivity and why cross-lab comparisons mislead
- Hold a fluent, informed conversation about LLM post-training, alignment, and where this fast-moving field is heading next
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Course content
15 sections • 15 lectures • 2h 11m total length
Requirements
- No programming, maths or machine learning experience needed. If you have used an AI chatbot and wondered how it was built, you have everything required
- No prior knowledge of reinforcement learning is assumed. We build it from scratch using everyday examples like learning to ride a bicycle
- Just curiosity and about two hours. Every concept is introduced with a real-world analogy before any technical term appears
Description
A machine has read almost everything humans have ever written. Ask it a simple question, and it cannot help you. Understanding why — and what we do about it — is the story of this course.
Every AI assistant you have ever used went through a second, far less understood stage of training after it finished reading the internet. That stage is where a raw text predictor becomes something you can actually talk to. It is where helpfulness, tone, refusals, reasoning and personality are decided. It is also where things go wrong: it is the reason models flatter you, pad their answers, and refuse perfectly harmless requests.
This course explains that stage from absolute zero, in plain English, with no mathematics and no prior knowledge of machine learning or reinforcement learning.
WHAT MAKES THIS COURSE DIFFERENT
Most material on this subject opens with equations. This one opens with a cook who has read every cookbook and never tasted food. Reinforcement learning is built up from learning to ride a bicycle. Reward models are explained through chess ratings. Over-optimisation is explained through schools teaching to the test. Every technical idea arrives only after you already feel the problem it solves.
The fifteen episodes tell one continuous story rather than fifteen disconnected lectures, and each one ends where the next begins.
WHAT YOU WILL UNDERSTAND
- Why a model that read the internet still cannot answer your question
- How instruction tuning turns a text predictor into an assistant, and where it hits a wall
- Why comparing two answers beats writing one, and how that single insight lifted the ceiling on AI quality
- What a reward model is, and how it learns human taste from comparisons alone
- Reinforcement learning explained from nothing: policy, reward, baseline, advantage
- What PPO, GRPO, RLOO and DPO actually disagree about
- Why models must be held on a leash, and what happens when they are not
- How verifiable rewards produced today's reasoning models
- Where preference data really comes from, and how human biases become permanent model behaviour
- Why AI turns sycophantic, verbose and over-cautious, and how to trace each behaviour to its cause
- How to read published benchmark scores with appropriate scepticism
WHO THIS IS FOR
Anyone who wants to understand modern AI properly rather than superficially: curious beginners, product managers and designers working alongside AI teams, engineers new to machine learning, students, writers and analysts.
By the end you will not just recognise these terms. You will be able to explain them, argue about them, and follow any conversation in this field with confidence.
Who this course is for:
- Anyone curious about how ChatGPT and similar AI assistants are actually built, with no technical background required
- Product managers, designers, founders and analysts working alongside AI teams who want to follow the conversation properly
- Software engineers new to machine learning who want the concepts behind RLHF before diving into papers or code
- Students and career changers exploring AI who want a solid conceptual foundation rather than a maths-heavy course
- Writers, journalists and researchers who need to discuss AI alignment and model behaviour accurately and confidently
- Anyone who has noticed AI being sycophantic, wordy or needlessly cautious and wants to understand exactly why it happens