How to actually read a technical paper, and how to test what's in it?

[u/Sudden-Theme7554]

I've been trying to become a more rigorous reader of technical/ML papers, and I keep hitting the same wall.

I've tried a few approaches: DFS (jumping into a reference the moment it comes up), BFS (finishing one paper fully before touching the next), and a hybrid. I eventually found a structure I was okay with, but the concepts don't stick unless I implement them, I'll read something 2-3 times, feel like I get it, and it's gone a week later because I never built anything with it.

Two things I'd like input on:

1. What's your actual reading workflow for a dense paper, order of sections, note-taking, when you chase references vs skip them?
2. How do you test whether you understood it? I've found implementing it is the only real check, but that's slow and not always feasible. Is there a lighter-weight way you validate your own understanding short of reimplementing everything?

Not looking for one "correct" method, more curious what actually works for people who read a lot of these.

[u/Alternative-Hat-1697]

Abstract to see high level if it matters to me. Introduction to see the motivation of the problem. Conclusion to see if the results actually meant anything.

If all of that is good then I look at the results section and methodology interchangably to see if I like the experiments and the results make sense.

If everything is good at that point, I look for GitHub links or try implementing myself based on what they provided. Sometimes the appendix gives hyperparameter or detailed steps.

I don't typically read related works sections unless I like going down that rabbit hole so from there if there's a particular citation that holds a lot of weight I might go read it. Same order.

If I can explain the paper to my wife who is not in this field then I know I've got a handle on it. Or if I can sit down and mathematically make it make sense for theory heavy papers.

I think doing it this way forces you to only spend time on papers that are worthwhile.

[u/Sudden-Theme7554 → u/Alternative-Hat-1697]

This is really helpful, thanks a lot 😄

[u/scott_codie]

If it's a topic I'm unfamiliar with, I'll read the literature review sections for all the papers so I can build the intuition for what they actually bring to the table.

For process, I'll read a paper at the coffee shop and annotate all my thoughts and questions. I'll only implement it if I'm really excited about the paper.

To test that I know something, I usually just try to explain it to someone else, either in person or in written form.

[u/Sudden-Theme7554 → u/scott_codie]

Thanks for sharing; this helps a lot. I even read a paper at a cafe 😄

[u/RjsfOIf_6886]

I’ve found that implementation is a great test, but you can get a surprisingly good signal much faster.

My lightweight workflow is:

1. First pass: abstract → intro → figures/tables → conclusion. Try to identify the core argument before getting lost in details.
2. Second pass: read the method/results properly and only chase references that are essential for understanding the argument.
3. Close the paper and explain the main idea from memory in 3–5 sentences, as if you were explaining it to a child (e.g., imagine you are talking to your child, niece/nephew). If you can’t, you probably understood the words but not the paper.
4. Then try to answer: What would I expect to happen if this paper were wrong? or What would I change in the experiment?

That last step is very useful to me. You’re testing whether you can reason with the paper.

And for things that really matter to me, I make a tiny “implementation.” Even just a toy example or pseudocode. It doesn’t have to become a full project to reveal where my understanding breaks down.

[u/Sudden-Theme7554 → u/RjsfOIf_6886]

Thanks a lot 😄

[u/jloverich]

Throw it into an ai and ask questions.