HunyuanProver turns math problems into formal proofs by learning from millions of examples.
HunyuanProver tackles data sparsity in theorem proving by combining scalable data synthesis with guided tree search, achieving state-of-the-art 68.4% accuracy on miniF2F benchmark.
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https://arxiv.org/abs/2412.20735
🤔 Original Problem:
→ Automated theorem proving faces major challenges due to limited training data and massive search spaces, making it difficult even for advanced LLMs to handle complex formal proofs.
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🔧 Solution in this Paper:
→ The framework introduces a two-pronged approach: scalable data generation and guided tree search algorithms.
→ Data generation starts with 130k natural language to LEAN statement pairs, doubles it through Chinese translation, and converts 30 million math problems to formal statements.
→ The system uses iterative tactic-level proving to generate new training data, expanding the dataset 40-fold.
→ Tree search employs multiple critic models: policy confidence for initial guidance, process reward model for proving possibility, and distance critic for step estimation.
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💡 Key Insights:
→ Explicitly trained critics significantly improve tree search guidance
→ Scale of finetuning data is crucial for theorem proving performance
→ Data curation and selection become vital with large training datasets
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📊 Results:
→ Achieved 68.4% accuracy on miniF2F-test benchmark, surpassing previous 65.9% SOTA
→ Successfully proved 4 IMO statements
→ Demonstrated effectiveness with less computational budget than competitors
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