They put a constraint on the budget, which means smaller models can generate more possible solutions than a big LM. In a nutshell, more solutions cover a larger number of problems solved with more diverse solutions even if there are more false positives.
They put a constraint on the budget, which means smaller models can generate more possible solutions than a big LM. In a nutshell, more solutions cover a larger number of problems solved with more diverse solutions even if there are more false positives.
Hi Devansh!
They put a constraint on the budget, which means smaller models can generate more possible solutions than a big LM. In a nutshell, more solutions cover a larger number of problems solved with more diverse solutions even if there are more false positives.
Did they give a reason why Smaller models would product better data?
Hi Devansh!
They put a constraint on the budget, which means smaller models can generate more possible solutions than a big LM. In a nutshell, more solutions cover a larger number of problems solved with more diverse solutions even if there are more false positives.