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arXiv · 2504.21230

Kimina Lean Server: A High-Performance Lean Server for Large-Scale Verification

Marco Dos Santos·Hugues de Saxcé·Haiming Wang·Ran Wang·Mantas Baksys·Mert Unsal·Junqi Liu·Zhengying Liu·Jia Li
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Abstract

We introduce the Kimina Lean Server, an open-source project designed as a high-performance verifier for reinforcement learning pipelines. Built on top of the Lean REPL (Read-Eval-Print Loop) maintained by the Lean FRO, our server combines server-side parallelism by managing multiple Lean processes in parallel with a Least Recently Used (LRU) caching mechanism that reuses Lean imports across requests. On the client side, a lightweight Python package enables submitting proof batches and receiving Lean feedback, including extracted tactics and tactic states. Together, these features enable a scalable workflow for large-scale verification and data extraction. In our experiments, the Kimina Lean Server outperforms previous Lean interaction tools, achieving a 1.5 to 2 times speedup in verification time. Moreover, its improved efficiency has enabled its use in the large-scale training of state-of-the-art models such as Kimina-Prover. We hope that our open-source project will support the neural theorem proving community and accelerate future progress by enabling efficient large-scale verification and proof data extraction.

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Marco Dos Santos, Hugues de Saxcé, Haiming Wang, Ran Wang, Mantas Baksys, Mert Unsal, Junqi Liu, Zhengying Liu, Jia Li. 2025-04-29. Kimina Lean Server: A High-Performance Lean Server for Large-Scale Verification. https://arxiv.org/abs/2504.21230

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