ROMEOADVANCED ACADEMY

Free Course · 5 lessons · ~3 hours

Reading an AI Paper Like a Researcher

A hands-on course on how working researchers actually read AI papers. Not the romanticised version. The pragmatic version — what to skim, what to study, when to put a paper down, and what to take away from one you finish.

Who this is for

You should take this course if you have ever opened a paper from arXiv, read the abstract twice, scrolled to the figures, felt lost, and closed the tab. You may be a software engineer who wants to keep up with the field. You may be a product manager who keeps hearing about results and wants to assess them yourself. You may be a student preparing to read papers for a thesis. You may be a working AI practitioner who reads papers regularly but suspects there are better techniques for it. The course is for all of you.

You do not need a doctorate. You do not need to be a strong mathematician — we cover only what you need to make a paper readable, not the inside of the proofs. You do need basic familiarity with machine learning concepts at the level of "I know what gradient descent is" and "I know what a transformer is, roughly." If you have taken Course 1 (Build Your First AI Agent) and Course 3 (AI Security Foundations), you are well-prepared for this one.

What you will learn

1

Why AI papers are hard, and how researchers actually read them

The volume problem. The layered reading strategy. The difference between how novices and experts read. The three-pass approach used by working researchers.

2

The anatomy of a modern AI paper

The standard sections — abstract, introduction, related work, method, experiments, ablations, appendix — and what each is actually for. What to skim. What to study.

3

Reading the method — the technical heart

Notation conventions. Architecture diagrams. Reading equations without panic. Pseudocode. Extracting the contribution from the boilerplate.

4

Reading the experiments critically

Benchmarks, baselines, ablations. The compute-spent trap. Cherry-picking signals. Generalisation claims. How researchers spot weak experimental evidence.

5

From reading papers to reading the field

Citation graphs. Reproducibility and code release. Maintaining a sustainable reading practice. The pitfalls of paper-overconsumption.

What you will need

  • About three hours of total time. Each lesson is 25 to 40 minutes.
  • A browser, and access to arxiv.org (free and open). We will work through three real published papers across the course.
  • Optionally, a free-tier account with Claude.ai or ChatGPT. Using an AI assistant as a reading partner — not a replacement — is covered in Lesson 3.
  • A notebook, paper or digital. Note-taking is part of the course; reading without writing is a slower path to understanding.

The papers we will read together

You do not have to read them in advance — we walk through each in the relevant lesson. They are listed here so you can take a quick look if you are curious.

  • Attention is All You Need (Vaswani et al., 2017). The transformer paper. We use it in Lesson 2 to map the anatomy of a paper, and in Lesson 3 to read a method section.
  • Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022). A more recent applied paper. We use it in Lesson 4 to read experimental sections critically.
  • One you pick. In Lesson 5 we ask you to pick a paper from the last six months and apply everything from the course to it. We will suggest options if you do not have one in mind.

What this course is not

This course is not a mathematics refresher. We do not teach the underlying maths of attention, gradients, or probability theory. We teach you how to read the maths in a paper well enough to follow the argument and judge the claims, even when you have not seen the technique before. If you want to deeply understand the maths, that is a separate (and worthwhile) journey we point you towards at the end.

How this fits with the full programme

This is a taster of the research craft developed across Path C — Applied AI Research, the Tier 3 specialisation for learners pursuing original research, contribution to open-source AI projects, or doctoral study. Path C is 60 ECTS across ten courses, covering mathematical depth, alignment, interpretability, frontier methods, experimental design, and reproducibility. Path C launches with the December 2026 cohort.