Notice that "significantly lower" can't actually be defined. There isn't a useful definition of overfitting which only requires observing a single model's train and test gap.^1 Here's a contrived but illustrative example: say model A has a training error rate of 0.10 and test error rate of 0.30. It's tempting to think "test error is 3x train error, we're overfitting". This may or may not be right; there absolutely could be a (more complex) model B with, e.g., training error rate 0.05, test error rate 0.27. Notice that the train-test gap increased going from A to B. But I don't care. Assuming these estimates are good, and all I care about is minimizing expected error rate, I'd confidently deploy model B over model A.

The useful definition of overfitting is that it refers to a region in function space where test error goes up and training error goes down (as model complexity goes up). Diagram (for underparametrized models). This definition tells us that the only good way to tell whether a model should be made more simple or more complex is to fit multiple models and compare them. This info is expensive to obtain for NNs, and obtaining it makes one look less clever. But it gives a reliable hint as to how a model should be iterated.^2 In the example above, if we really did observe that model B, then perhaps our next one should be even more complex.

If you're asking more specifically about reading NN loss curves, I haven't seen any science which puts claims like #4 here to the test.^3 I'd also like to mention another common issue w/ reading NN loss curves: people usually don't take care in estimating training loss. The standard NN training loop results in overestimates, which will make the gap between training and validation appear bigger than it actually is. I happened to write about this problem today, here in CrossValidated.

Footnotes

1. 2 exceptions to this: (1) you're already quite familiar w/ the type of task and data, so you can correlate high gaps with overfitting based on previous experience (2) test error is higher than an intercept-only model, and training error is much lower.

2. Double descent complicates this workflow. For overparametrized models like NNs, one can be deceived into not going far enough when increasing model complexity. Or it's difficult to determine whether a certain intervention is actually increasing or decreasing complexity. This paper characterizes various forms of double descent.

3. My answer to #4 would be the same as what I wrote when criticizing the caption in my first comment. The provided answer—"reduce model capacity"—is too vague. The answer should be: select the model checkpoint from halfway, simply b/c its test error is the lowest. The graph alone doesn't tell you anything about how the model should be iterated beyond that info. That's b/c each point on the curve is the loss after being trained for n iterations, conditional on all of the other factors which modulate the model's complexity. There could absolutely be a model w/ more depth, more width, etc. which performs better than the simpler model trained halfway.