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Leanabell-Prover: Posttraining Scaling in Formal Reasoning
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Recent advances in automated theorem proving (ATP) through LLMs have highlighted the potential of formal reasoning with Lean 4 codes. However, ATP has not yet be revolutionized by the recent posttraining scaling as demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the entire posttraining of ATP, aiming to align it with breakthroughs in reasoning models in natural languages. To begin, we continual train current ATP models with a hybrid dataset, which consists of numerous statement-proof pairs, and additional data aimed at incorporating cognitive behaviors that emulate human reasoning and hypothesis refinement. Next, we explore reinforcement learning with the use of outcome reward returned by Lean 4 compiler. Through our designed continual training and reinforcement learning processes, we have successfully improved existing formal provers, including both DeepSeek-Prover-v1.5 and Goedel-Prover, achieving state-of-the-art performance in the field of whole-proof generation. For example, we achieve a 59.8% pass rate (pass@32) on MiniF2F. This is an on-going project and we will progressively update our findings, release our data and training details.
Forward citations
Cited by 6 Pith papers
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Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
Agentic LLM framework autoformalizes 32 Putnam problems and main theorems plus proofs from five STOC papers into Lean 4, with two proofs using only kernel axioms.
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Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
An orchestrator-driven agentic pipeline using general coding LLMs autoformalizes 32 PutnamBench problems and the main theorems plus proofs from five STOC papers into Lean 4, with two proofs using only the kernel.
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SCOPE: Leveraging Subgoal Critiques for Code Generation
A Lean-oriented prover model, fine-tuned with dense and sparse RL rewards, generates structured semantic critiques that improve LLM code generation accuracy over Reflexion and Self-Refine on LiveCodeBench V6 and BigCodeBench.
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Aristotle: IMO-level Automated Theorem Proving
Aristotle reaches gold-medal-equivalent performance on 2025 IMO problems via integrated Lean proof search, informal lemma formalization, and a dedicated geometry solver.
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On Reasoning-Centric LLM-based Automated Theorem Proving
ReCent-Prover achieves a 22.58% relative improvement over prior state-of-the-art in proved theorems on the CoqStoq benchmark by using reasoning-centric techniques under a fixed LLM invocation budget.
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AI for Mathematics: Progress, Challenges, and Prospects
AI for math combines task-specific architectures and general foundation models to support research and advance AI reasoning capabilities.
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