this post was submitted on 21 Jul 2026
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The article quotes a post from ISBNdb saying the issue is model collapse from training on synthetic data.
Total collapse is a solution.
It's also unavoidable.
That's like saying, "they had some failure modes from the synthetic data, so they should just obviously stop trying forever."
They'll just fix the edge cases and move on. Like any programming task.
The problem is that synthetic data is not fit for that purpose. The more of it you use, the worse at dealing with the cases LLMs get.
Think of it like this... You feed a language model a bunch of genuine human-written content. Great. Now it can produce the most likely text in a lot of cases. Word combinations that rarely appear in written language rarely get generated, so most of its synthetic data lacks those rare - but still valid - combinations.
Train it on this synthetic data, and now more outliers and rare combinations get filed off. Rinse and repeat.
I think what they're saying is that the AI companies don't need to rinse and repeat. They've already created solid programs for how to make LLMS speak English and demonstrate basic reasoning. You don't need to retrain that part every time. Once you've trained "English.exe," you can just copy it endlessly.
Maybe with enough time, the hard-coded English of the LLMs could become increasingly anachronistic and sound old-fashioned and formal to most ears. But for that kind of for slow maintenance you could just pay people to write examples of modern language and train it on that.
Yes, what's the issue?