And AI changes so quickly that there is a breakthrough every week.
Call my cynical, but I think this is an RLHF/RLVR push in a narrow area--IMO was chosen as a target and they hired specifically to beat this "artificial" target.
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And AI changes so quickly that there is a breakthrough every week.
Call my cynical, but I think this is an RLHF/RLVR push in a narrow area--IMO was chosen as a target and they hired specifically to beat this "artificial" target.
https://benture.io/job/international-math-olympiad-participa...
https://job-boards.greenhouse.io/xai/jobs/4538773007
And Outlier/Scale, which was bought by Meta (via Scale), had many IMO-required Math AI trainer jobs on LinkedIn. I can't find those historical ones though.
I'm just one piece in the cog and this is an anecdote, but there was a huge upswing in IMO or similar RLHF job postings over the past 6mo-year.
If you looked at RLHF hiring over the last year, there was a huge hiring of IMO competitors to RLHF. This was a new, highly targeted, highly funded RLHF’ing.
Is this some kind of meta-level play to sound less fake?
Not sure if it’s embarrassing or a fundamental limitation that grooming and misunderstanding satirical articles defeat the models.
Because people ask LLMs all of these things, including arithmetic. People were saying the same about the number of r's in strawberry. Why ask and LLM that!?!? But the big AI companies want LLMs to be better at these questions, probably because people ask them to LLMs. The big AI companies want this because there is no other explanation for the money poured into RLHF'ing these types of problems.
exactly. The opposite is also true. You might supply more clarifying information to the LLM, which would help any human answer, but it actually degrades the LLM's output.
IME Reddit would scream "tokenization" at the strawberry meme until blue in the face, assuring themselves better tokenization meant the problem would be solved. Meanwhile RLHF'ers were/are en masse paid to solve the problem through correcting thousands of these "counting"/perfect syntax prompts and problems. To me, since RLHF work was being paid to tackle these problems, it couldn't be a simple tokenization problem. If there was a tokenization bottleneck that fixing would solve the problem, we would not be getting paid to so much money to RLHF synax-perfect prompts (think of Sudoku type games and heavy syntax-based problems).
No, why models are better are these problems now is because of RLHF. And before you say, well now models have learned how to count in general, I say we just need to widen the abstraction a tiny bit and the models will fail again. And this will be the story of LLMs forever--they will never take the lead on their own, and its not how humans process information, but it still can be useful.
In a sense this is wrong. Some say the indispensability of modern math, which includes the incomputable reals, shows that such abstract objects are required for science.
I mean on LinkenIn you can find many AI trainer companies and see they hire for every subject, language, and programming language across several expertise levels. They provide the laborers for the model companies.
If I’m a contractor forced to use Chrome and mobile devices, can I deduct a separate work phone?
I really hate having it my iPhone, at least maybe I can claw something back this way?
But, you still have to read it:
> Reading other people’s code is part of the job...I have to read the code line-by-line anyways.
So instead of writing the tedious code, I only have to read it. Oh but don't worry, I don't have to read it too carefully because:
> Agents lint. They compile and run tests. If their LLM invents a new function signature, the agent sees the error
But remember...
> You’ve always been responsible for what you merge to main.
So now I have to oversee this web of agents and AI ontop of coding? Am I doing more now for the same pay? Am I just speedrunning myself toward lower pay? Is AI adoption a prisoner's dilemma toward lowing my wages hardest?
Because is good at coding compared to many other disciplines (e.g. math), it makes the internal AI politics among programmers more of an issue. Add fuel to that fire baby!