If you watch how agents attempt a task, fail, try to figure out what went wrong, try again, repeat a couple more times, then finally succeed -- you don't see the similarity?
LLMs don't do this. They can't think. If you just one for like five minutes it's obvious that just because the text on the screen says "Sorry, I made I mistake, there are actually 5 r's in strawberry", doesn't mean there's any thought behind it.
If that's not thinking, then I don't know what is.
> If that's not thinking, then I don't know what is.
How about actual thinking, you know, what humans and to a lesser extent animals do?
You can also take it a step further and add automatic fine-tuning once you start gathering a ton of data, which will rewire the model somewhat.
But in my mind, if I tell the LLM to do something, and it did it wrong, then I ask it to fix it, and if in the future I ask the same thing and it avoids the mistake it did first, then I'd say it had learned to avoid that same pitfall, although I know very well it hasn't "learned" like a human would, I just added it to the right place, but for all intents and purposes, it "learned" how to avoid the same mistake.