I’m guessing something like a 0.1% success rate. I think this is sufficient for success if you have automated the process and can afford to run the process enough to generate and test millions of possibilities. This is a largely parallelizable process, so it doesn’t necessarily take much wall clock time.
How much compute would it take to test a million of these in parallel? I assume you’re imagining something less compute-intensive than retraining a million GPTs from scratch, but I’m unclear how much less compute-intensive.
How much evidence does it need per instance to figure out whether the change is an improvement? With a 0.1% success rate, it doesn’t take much imperfection in the evaluations for most apparent improvements to be false positives.
How much compute would it take to test a million of these in parallel? I assume you’re imagining something less compute-intensive than retraining a million GPTs from scratch, but I’m unclear how much less compute-intensive.
How much evidence does it need per instance to figure out whether the change is an improvement? With a 0.1% success rate, it doesn’t take much imperfection in the evaluations for most apparent improvements to be false positives.
I respond to you and Max in my other comment. https://www.lesswrong.com/posts/zwAHF5tmFDTDD6ZoY/will-gpt-5-be-able-to-self-improve?commentId=bB2ssvhEjjsPovuTh