zlacker

This is a really interesting "data flywheel" -- better model >> more usable data >> even better model

>>levoca+(OP)
surely there's an upper limit to this though with models literally eating themselves.

>>tills1+o9
When a human students learns to read more carefully we don't consider that a negative.

>>tills1+o9
We can wait for that to start appearing in tests or benchmarks first.

>>tills1+o9
They already purposely train them on their own output, it's called synthetic training data.

>>tills1+o9
Not always, you can improve the loop by putting something real inside, like, a code execution tool, a search engine, a human, other AIs or an API. As long as the model can make use of that external environment its data can improve. By the same logic a human isolated from other humans for a long time might also be in a situation of going crazy.

Practical example - using LLMs to create deep research reports. It pulls over 500 sources into a complex analysis, and after all that compiling and contrasting it generates an article with references, like a wiki page. That text is probably superior to most of its sources in quality. It does not trust any one source completely, it does not even pretend to present the truth, it only summarizes the distribution of information it found on the topic. Imagine scaling wikipedia 1000x by deep-reporting every conceivable topic.