This might sound a bit corny. I try to have a sparse BFS understanding of the field at any given time and a DFS on topics I'm interested in like interpretability, NLP, and GNNs.

Four things that I think are important are - contributing to open source, joining discord communities, at the very min "skimming" papers(reading abstracts, conclusions, and charts), and I also topic model researchers' Github repos i find on paperswithcode. As a 5th - ML Twitter if you can maintain your sanity.

The sixth sense and the most important one is to have a strong math background, IMO it is the most important aspect that helps generalize new research. grok linear algebra, probability, and calc. Mostly linear algebra though because the tensor notation really helps with probability and functional analysis. A lot of physics can be understood through the lens of tensor analysis and probability.