OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
OptProver transfers formal theorem proving from Olympiad math to optimization via continual training, achieving SOTA Pass@1 and Pass@32 on a new Lean 4 benchmark while retaining general performance.
AI for math combines task-specific architectures and general foundation models to support research and advance AI reasoning capabilities.
citing papers explorer
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving
Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
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OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
OptProver transfers formal theorem proving from Olympiad math to optimization via continual training, achieving SOTA Pass@1 and Pass@32 on a new Lean 4 benchmark while retaining general performance.
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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.