ImProver 2: Iteratively Self-Improving LMs for Neurosymbolic Proof Optimization
This work addresses the need for scalable proof optimization in formal mathematics, benefiting researchers and developers working with large formal libraries.
ImProver 2 introduces a neurosymbolic framework for automated proof optimization in Lean 4, combining expert-iteration with a scaffold that exposes formal structure and informal abstractions. A 7B-parameter model trained with this approach outperforms much larger models and is competitive with mid-tier frontier models across multiple metrics.
Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data quality for neural provers. However, scalable proof optimization is hindered by heterogeneous and heuristically specified objectives, scarce data, and high training and inference costs. To overcome these challenges, we introduce ImProver 2, a neurosymbolic framework for automated proof optimization in Lean 4. ImProver 2 combines a data-efficient expert-iteration pipeline with a scaffold that exposes formal structure alongside lightweight informal abstractions. We further introduce a suite of metrics capturing structural proof properties. Using ImProver 2, we train a 7B-parameter model that outperforms orders-of-magnitude larger models within the same model family, and is competitive with mid-tier frontier models across metrics. We additionally demonstrate that our neurosymbolic scaffold significantly improves performance across both small and frontier models. We show that with proper scaffolding and training, small models can effectively restructure research-level proofs over complex and varied metrics, matching substantially larger systems and establishing proof optimization as a scalable, learnable task.