I'm starting to think loss is harmful. Our loss has been a flat 2.2 for the past five days training GPT-2 1.5B. Yet according to human testing, it's been getting noticeably better every day. It's now good enough to amuse /r/dota2: teddit.net/r/DotA2/comments/…

Dec 31, 2019 · 11:40 PM UTC

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The dota2 data is only 0.73% of the overall training data (73MB out of 10GB). Yet the bot is adept enough to convince /r/dota2 that it's talking about dota. Again: Loss has been a flat 2.2 for the last five days. And five days ago, the model wasn't this good.
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The history of science shows that when something seems out of place, we should pay close attention. Loss != quality. This topic deserves thorough analysis. And as far as I know, no one has done it yet. Otherwise people wouldn't be using loss as a quality metric.
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Replying to @theshawwn
I wonder if there is a connection to ”deep double descend” openai.com/blog/deep-double-…
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Replying to @theshawwn
Are there other interesting metrics that are evolving and visible? Distribution of weights, some entropy measures, etc.
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Replying to @theshawwn @heghbalz
This would suggest that the weights still travel to "better" arrangement despite near constant loss. I thought that at that point it's more of a random walk.
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Replying to @theshawwn
Is the loss evaluated on dota2 data only or the whole corpus? It may only progress on dota2 and stall on the remaining
Replying to @theshawwn
Something I don't understand here: the optimizer goal is to minimize the loss no? So how can it converge towards better solutions without actually reducing the loss? What tells it "this is a better sol" if the only measure it has for the direction is the gradient of the loss?
Replying to @theshawwn
Need to train a model to detect the 'getting better' trend here.
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