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I agree that LLMs are unlikely to be the final form for AGI, but what you are talking about is orthogonal to the IQ and for most cases general utility. It's like looking at a savant chained to a workstation reading tasks from a conveyor belt and saying that it will never have human level capabilities.

Wouldn't agents that do inference in an infinite loop pass that bar?

Often the smartest thing is to do nothing.

Or know when to shut up.

A tangent, but can anyone ELI5 how models "know" when to stop generating tokens? Or what the method to stop them at the right point is?

The model doesn't "know" how to generate tokens any more than it knows how to stop generating tokens. The sampler simply stops pulling values when it outputs a "stop token", which is a token the same way every other token is.

That is to say, it stops when it's statistically the most likely to.

OK thanks, so the neural net (that no one can explain fully) generates a "stop" signal at a certain point.

I might be out of date but my understanding was that STOP was just another token that gets predicted.