Humans get smarter by thinking. In particular they deduce correct conclusions from known premises or notice and resolve internal inconsistencies. As long as they are more likely to correct wrong things to right things than vice versa, they converge to being smarter.
AIs are close or at that level of ability, and as soon as it’s taken advantage of, they will self improve really fast.
Yes. Current reasoning models like DeepSeek-R1 rely on verified math and coding data sets to calculate the reward signal for RL. It’s only a side-effect if they also get better at other reasoning tasks, outside math and programming puzzles. But in theory we don’t actually need strict verifiability for a reward signal, only your much weaker probabilistic condition. In the future, a model could check the goodness of it’s own answers. At which point we would have a self-improving learning process, which doesn’t need any external training data for RL.
And it is likely that such a probabilistic condition works on many informal tasks. We know that checking a result is usually easier than coming up with the result, even outside exact domains. E.g. it’s much easier to recognize a good piece of art than to create one. This seems to be a fundamental fact about computation. It is perhaps a generalization of the apparent fact that NP problems (with quickly verifiable solutions) cannot in general be reduced to P problems (which are quickly solvable).
Humans get smarter by thinking. In particular they deduce correct conclusions from known premises or notice and resolve internal inconsistencies. As long as they are more likely to correct wrong things to right things than vice versa, they converge to being smarter.
AIs are close or at that level of ability, and as soon as it’s taken advantage of, they will self improve really fast.
Yes. Current reasoning models like DeepSeek-R1 rely on verified math and coding data sets to calculate the reward signal for RL. It’s only a side-effect if they also get better at other reasoning tasks, outside math and programming puzzles. But in theory we don’t actually need strict verifiability for a reward signal, only your much weaker probabilistic condition. In the future, a model could check the goodness of it’s own answers. At which point we would have a self-improving learning process, which doesn’t need any external training data for RL.
And it is likely that such a probabilistic condition works on many informal tasks. We know that checking a result is usually easier than coming up with the result, even outside exact domains. E.g. it’s much easier to recognize a good piece of art than to create one. This seems to be a fundamental fact about computation. It is perhaps a generalization of the apparent fact that NP problems (with quickly verifiable solutions) cannot in general be reduced to P problems (which are quickly solvable).