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Researchers introduce ImProver 2, a neurosymbolic framework that trains a 7-billion-parameter model to automatically optimize formal mathematical proofs in Lean 4, outperforming much larger models in the same family. The framework combines expert-iteration pipelines with formal structure scaffolding and new metrics for measuring proof quality, demonstrating that smaller models can effectively restructure research-level proofs when properly trained.
Why it matters: As formal mathematics libraries expand, automated proof optimization becomes critical for maintainability and training neural provers; this work shows that efficiency gains through better scaffolding and training could democratize access to powerful proof-optimization capabilities without requiring frontier-scale models.
