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arXiv CS.AI·1d ago·5 min read
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning
TL;DR
Sequent-Prover is a framework that trains agents to perform formal reasoning using SMT-LIB programs, significantly improving their accuracy and efficiency. Post-training enhancements increased the model's accuracy from 85.6% to 97.0% and reduced the number of solver calls.
✦ Why It Matters
Consider integrating Sequent-Prover agents into your formal verification workflows to enhance accuracy and efficiency.
Key Takeaways
- Explore using Sequent-Prover for formal reasoning tasks.
- Leverage the improved accuracy of 97.0% in your applications.
- Reduce solver calls to enhance computational efficiency.
- Implement trajectory distillation for better model training.
How It Works
Sequent-Prover agents operate by writing and executing SMT-LIB programs, which are formal representations of logical problems. They interact with the Z3 solver to validate their reasoning, iteratively refining their approach based on feedback.
The verifier-conditioned trajectory distillation process allows the agents to learn from successful reasoning paths, ensuring that they produce correct and executable solutions.