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MIT researchers, in collaboration with KAUST and HUMAIN, have released MathNet, the largest open-source dataset of Olympiad-level math problems, containing over 30,000 expert-authored problems from 47 countries.
OpenAI achieved a new state-of-the-art 41.2% on the miniF2F formal math olympiad benchmark using a technique called 'statement curriculum learning,' which iteratively trains a neural prover on proofs of increasing difficulty. The approach builds on iterative proof search and retraining over 8 iterations to significantly outperform the previous best of 29.3%.