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DecompRL enables LLMs to solve complex problems by breaking them down into manageable sub-tasks, achieving a 50x reduction in GPU costs while enhancing solution diversity.
Graduate-level mathematics can now be auto-formalized at scale, opening the door to automated verification of both human and AI-generated proofs.
LLMs can now automatically verify imperative code at scale, achieving state-of-the-art results on challenging verification benchmarks and paving the way for large-scale verified code datasets.
