Agent-directed tree search improves LLM performance on Lean formal verification tasks, with context-based orchestration solving more intermediate specs at lower token cost than baseline agents.
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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.
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.
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