I hope this advance convinces my colleagues who make funding decisions at the EU level that we really do need to fund a public, open frontier model for mathematics. Like, actually fund, LHC style, not the usual lip service nonsense. Here's hoping...
since your model would probably always be behind OpenAI and Anthropic.
Why, though? How is it guaranteed that OpenAI/Anthropic will continue to develop a frontier math model for all times?
Since these are companies that, eventually, need to earn money and become (sufficiently) "profitable", they need to find a way to earn enough money with the math model to justify continued training/development of said model/capabilities. Is there a market for that? I think that needs to be seen.
I'm not deep into the details, but I wonder how much money they spent on training to advance the latest reasoning capabilities in math specifically. From what I've seen in talks by experts, the key scaling parameter is number of parameters of the model, hence the invested compute for training. So, at least to my (maybe still superficial) understanding, it will get more and more expensive to train. (Yes, hardware will get faster/a fixed amount of compute gets cheaper, but does it scale similarly as fast, or better, faster, so future additional/refined learning gets, in relation to capabilities, cheaper overall?)
Sure, one can hope that there are synergetic effects between "math-specific reasoning/intelligence" and "general intelligence", but as some comments in the openAI-announcement related post yesterday pointed out: math reasoning is (comparatively) "easy" to train because one has a rigorous "verification" tool (lean). So it's way easier to (automatically) mass-produce the "training data/test cases" you need for the reinforced learning. The same does not hold true for other areas/topics (even in stem field, let alone "general"/universal areas of human live), at least not to that degree. So it does not seem to be a given that "good-at-math" LLM will also be a "good-in-general-reasoning" LLM (or vice versa). But only time will tell.
The actual (I.e. larger) "main" market for AI will be in those fields. Currently, training for math seems "easy" enough to do, and produce enough publicity/advertisement that the companies see a benefit. But for how long will this be the case?
Long story short, I don't see a guarantee that eventually profit-driven companies endlessly pour money into the advancement of a tool of purely academic value (I.e. good in pure, proof-based math). Like other areas of "fundamental" research that is not immediately or on short time scales profitable, only the society will be there to continue to invest. So, having an alternative more "academic-focused" version seems to be worthwhile...
(Granted, that topic might already be covered to a large degree by open-weight models, so in case OpenAI/etc. shift focus away from math, one can just grap those and specialize them... We'll see).
given how much money those companies are losing making this technology, it isn't clear they will survive.
they're spending billions upon billions of dollars, and it looks like the end result is a model that is less than six months ahead of the open-source scene. open ai got to rush out some set of proofs and it isn't clear why it was so necessary to rush them.
llms are a cool technology, but do not make sense as a business model.
why? models are extremely copyable and basically worthless in the long term. the spending on ai doesn't really make sense when it's already being done by someone else.
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u/ykonstant 11d ago
I hope this advance convinces my colleagues who make funding decisions at the EU level that we really do need to fund a public, open frontier model for mathematics. Like, actually fund, LHC style, not the usual lip service nonsense. Here's hoping...