It took a lot of work to extract any information from a paper; I had to spend hours reading it. I was always impressed at folks who could just glance at a single figure, without referring to the methods, and could glean what the paper was trying to say.
However, after working through enough papers and replicating the results of the authors, I came to learn a number of things- 1) papers are just written badly and it's not beacuse the authors are smart. It's beacuse the authors are bad writers. 2) most papers- in bio, I'd say about 90%- contain invalidating errors which mean that the figures and conclusions are worthless. It takes skilled readers to uncover methodological flaws (or infer them, as often not all the details are included).
After working in bio for a while, it was nice to be in ML because - at least it seemed- I could replicate most papers by downloading the github repo, training on my local GPU for a few days, and then using the trained model to make the same predictions as the papers. Then I realized- in most cases, what was being claimed was far more than what the trained model was actually capable of doing.
Now I stick to well-trod engineering literature that most people consider boring. In nearly all cases I can read the lit, repro the work, and get results that make sense (much of my work is ensuring that published benchmarks are reproducible).
The advice here is golden.