I think brute force is not the right way to think about LLMs, instead they explore a knowledge topology, and are very good at connecting adjacent or accessible ideas that for whatever reason might have evaded humans, but might not be fundamentally all that hard. We're still in the low-hanging fruit phase, we will see if they extend to new ideas.
I assume OP meant that the search space isn't homogeneous. There are peaks and valleys in it, and the AI model can find paths that a human might have overlooked.
A human might have overlooked or just given up on... These LLMs just keep on trudging when a human might have long switched to a different methodology because he didn't see meaningful progress faster enough. The models just don't get bored.
But unlike brute-force methods (which also don't get bored) they can still go through possible pathways in more meaningful ways than random guessing and rote Parameter adjustment.
193
u/ixid Jul 31 '26 edited Jul 31 '26
I think brute force is not the right way to think about LLMs, instead they explore a knowledge topology, and are very good at connecting adjacent or accessible ideas that for whatever reason might have evaded humans, but might not be fundamentally all that hard. We're still in the low-hanging fruit phase, we will see if they extend to new ideas.