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I work with LLMs daily. I read papers as they hit arxiv. Also daily. You clearly don’t.
I’m not interested in convincing anyone, which is why I’m speaking non-technically.
The benchmarks being cited aren’t as interesting as you appear to believe they are. You’ve not fully grasped the fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt. For OpenAI to also be embroiled in allegations of plagiarism only serves to underscore the gap between the two concepts.
I understand that, but the original statement was about the models stalling out in progress.
Would you say a student stalled out in progress if they could barely do 2 + 2 a couple years ago and is now able to consistently solve some of the most complex math problems known just because that student isn’t creating novel research?
You’re trying to analogize your way into a subject you clearly haven’t studied.
There’s pre-existing research here. Godel’s Incompleteness Theorem holds, plus others.
There’s already a known upper bound here that you’re clearly unaware of.
There’s as yet been zero LLM-based architectures that have created new information. Everything they produce is somewhere within the training data. LLMs are a very specialized data compression algorithm, in a fashion.
The stall is around whether Recursive Self-Improvement is achievable. Recent papers out of China are trying to chart a course to it. But, until someone succeeds, The current pace of improvement is already slowing signs of slowing. It’s not about where the finish line is placed, it’s about how fast they get there.
How are you defining “new information”? If I ask Claude "what is 235 x 567 + 57,899? " There is a high likelihood that that question was not in its training data, but it still gets it right and generates new information. It will be using existing methods from its training data to “reason” to get the answer, and it hasn’t shown the ability to devise new methods. But 99.99% of the problems in the world can be solved by using existing methods.
Open AI isn’t going to go bust because it can’t make novel math techniques, almost all of the knowledge work requiring math doesn’t require new methods. If it’s able to master every existing technique used by knowledge workers without creating new methods, ie. your finish line, then they’ll have automated a huge chunk of labor and probably become a hugely profitable company.
Recursive self improvement may not be panning out currently so we won’t see any singularity style exponential take off, but we’re still seeing steady linear improvement in tasks such as math and reasoning through other techniques, like the opaque recurrence that astra is using.
As has been stated, this isn’t new territory. If you’re unfamiliar with information theory, you have some reading to do.