Earlier quoted context omitted.
This quote from the article sounds like it learns from failed tests: >We trained AlphaProof for the IMO by proving or disproving millions of problems, covering a wide range of difficulties and mathematical topic areas over a period of weeks leading up to the competition. The training loop was also applied during the contest, reinforcing proofs of self-generated variations of the contest problems until a full solution…
Reading between the lines a bit, that does answer the question I had though don't think I I clarified very well. I read that to say the model's token weights are adjusted as it goes, so in an LLM sense it is kind of learning. It isn't reasoning through an answer in the way a human does though. Meaning, the model is still just statistically predicting what an answer may be and checking if it worked. I wouldn't chalk t…
I'd be hard to know how many failed attempts the human made. Humans are constantly thinking of ideas and eliminating them quickly. Possibly to fast to count.