This response might be slightly misleading, unless I’m misunderstanding the other posts around this. RG is not a factor in training, but it is in testing. ( many might not know how ML works…so understanding of the word “training” might anyway encompass testing for some readers ) …That might sound pedantic! 
But for me, as mentioned, its a core incentive, so I was happy to learn more about how it was working this week ( and will be happy if someone corrects this with further info).
My current understanding :
HELPING FIX MISSING SPECIES with less than 100 photos
If there are less than 100 photos of a species… like @matthias55, we can try to help train it.
We do not need to reach RG, just accumulate the 100. This should be roughly visible through exploring observations though, no need to use API(?)…
e.g. on a blank I think I’ve nearly filled… :
https://www.inaturalist.org/observations?place_id=any&subview=table&taxon_id=451684
30 obs with 1-6 photos should be approaching the necessary 100 total.
Currently, CV suggest is some sort of ant for this species, so wrong taxonomic order entirely…and a nice sense of achievement to fix I think!
HELPING FIX INCORRECT SPECIES with over 1000 photos
If however, over 1000 photos already exist and a user rather wishes to help fix a recurrent error with the CV, adding more won’t necessarily help… this is more about ensuring there is a cleanliness to the existing test dataset, in which case, helping with quality control, as an identifier, might contribute to resolve the issue more directly and prevent more incorrect obs being placed in the dataset.
A core issue at present - as visible in the computer vision clean-up wiki - seems to be issues which have been created due to this feedback loop of misidentification >> wrong auto-suggest >> further misidentification.
HELPING FIX INCORRECT SPECIES with 100-1000 photos
If there are between 100 and 1000 photos for the species set, helping with identification quality control and increasing the amount of training data both seem like valid ways to help. Both should contribute to overall accuracy. If I understand correctly.