Exactly.  After 100 samples, the loss is identical, but after 200 samples, you can start to see it slightly diverging as shown here:
After 200 samples:

outputs: Y_hat: tensor([0.8876], grad_fn=<ViewBackward>), Y: tensor([1.]), loss: 0.11922011524438858
outputs: Y_hat: tensor([0.8876], grad_fn=<ViewBackward>), Y: tensor([1.]), loss: 0.11922004818916321

By 700 or so it’s obvious.  After 30 epochs over 3000 samples, the evaluation shows the models make different choices:

Fscore(keys=986, cor=963, mis=10, spu=13) 0.988 F  (0.987 P / 0.990 R)
Fscore(keys=986, cor=974, mis=10, spu=2) 0.994 F  (0.998 P / 0.990 R)

That being said, it’s possible those 9 additional errors were all the same kind of thing, or whatever. But the point is it’s definitely different.
Just wanted to see if anything else might be going on.