Leanabell-Prover: Posttraining Scaling in Formal Reasoning
Jingyuan Zhang, Qi Wang, Xingguang Ji, Yahui Liu, Yang Yue, Fuzheng Zhang, Di Zhang, Guorui Zhou, Kun Gai·April 08, 2025
Summary
Research enhances automated theorem proving with Lean 4, focusing on posttraining scaling. ATP models, trained on a hybrid dataset, surpass baselines with a 59.8% passing rate on MiniF2F. The study tackles formal language reasoning challenges, improving performance through posttraining strategies. It corrects an initial flawed proof for a distinct integer list problem, suggesting valid solutions like powers of 2, specific element relationships, or a factorial approach. The Leanabell-Prover-GD-SFT model's valid proof distribution is analyzed.
Introduction
Background
Overview of automated theorem proving (ATP)
Importance of Lean 4 in ATP
Objective
Focus on posttraining scaling in ATP models
Improvement in performance through posttraining strategies
Method
Data Collection
Hybrid dataset used for model training
Data Preprocessing
Techniques applied to prepare data for training
Model Training
Description of the ATP models trained on the hybrid dataset
Posttraining Scaling
Strategies employed to enhance model performance
Techniques for optimizing ATP models after training
Results
Performance Evaluation
Comparison with baseline models
59.8% passing rate on MiniF2F benchmark
Formal Language Reasoning
Challenges addressed in formal language understanding
Proof Correction
Initial flawed proof for distinct integer list problem
Valid solutions identified: powers of 2, specific element relationships, factorial approach
Leanabell-Prover-GD-SFT Model Analysis
Distribution of valid proofs generated by the model
Discussion
Insights on Posttraining Strategies
Impact of posttraining on ATP model performance
Formal Language Reasoning Enhancements
Improvement in handling formal language complexities
Proof Generation and Validation
Analysis of proof generation mechanisms
Future Directions
Potential areas for further research and development
Conclusion
Summary of Findings
Recap of the research outcomes
Implications
Impact on the field of automated theorem proving
Recommendations
Suggestions for future studies and practical applications
Basic info
papers
artificial intelligence
Advanced features
Insights
What innovative approaches are introduced in the research to address formal language reasoning challenges?
How does the study utilize posttraining strategies to enhance the performance of ATP models?
What limitations are identified in the study regarding the Leanabell-Prover-GD-SFT model's proof distribution?
What are the main objectives and findings of the research on automated theorem proving with Lean 4?