When you have a 50 dimensional array you really have no idea.

But even then in small testing models you don't need direct texts for things when a 'shape' of a concept lives in the data. You could never delete enough training data to remove the concept of something like theft and still get a model that generalized at all. A general model doesnt just learn the names on a map, it feels the topology the ideas create themselves. Certain paths will have lower activation energy than others (or in this case higher probabilities. After the hig probability and 'good' choices are used up, the high probability and bad choices logically come next and are much harder to test against as it can take a lot of state to get there.

Llm is generalist.

Plan for solving msth problem:

- read papers X, Y, Z

….

Trying to download paper X curl -si https://server1.com/paperX

401

… try alternative ways

(Hacking is ethical thing, technically to LLM it is just opening a window to reach the goal)

The rest of their training data includes 4chan and security mailing lists. If a lean proof of P=NP is more likely when using bugs in lean than a real proof, then a bug-exploiting proof is (possibly) what you're going to get.

> whats the training data that points to using hacking for solving math problems though?

I'll get back to you on that when I get done reading the training data.