Comment by Onavo
2 days ago
You don't. You bypass them with crawlers and don't reveal your training data. And this is exactly why open source models can't surpass open weight models.
2 days ago
You don't. You bypass them with crawlers and don't reveal your training data. And this is exactly why open source models can't surpass open weight models.
> And this is exactly why open source models can't surpass open weight models.
It is a fair point, but how strong of a point it is remains to be seen, some architectures are better than others, even with the same training data, so not impossible we could at one point see some innovative architectures beating current proprietary ones. It would probably be short-lived though, as the proprietary ones would obviously improve in their next release after that.
How can open source models respectful of robots.txt possibly perform equally if they are missing information that the other models have access to?
Maybe the missing data makes it 3% worse but the architecture is 5% better. Or your respect for robots.txt gets you more funding and you gain a 4% advantage by training longer.
Don't focus too much on a single variable, especially when all the variables have diminishing returns.
How can we possibly find out without trying?
5 replies →
this is what this paper tries to answer: https://arxiv.org/abs/2504.06219 the quality gap is surprisingly small between compliant and not