VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search

Overview

Explore VerMCTS, a novel approach combining verifiers, LLMs, and Monte Carlo Tree Search to generate verified programs in Dafny and Coq, showing significant improvements over base language models.

Syllabus

• Introduction to VerMCTS
Overview of VerMCTS methodology
Key components: Verifiers, Large Language Models, Monte Carlo Tree Search
Application and significance in program synthesis
• Fundamentals of Verifiers
Introduction to Dafny and Coq
Techniques for verification in program synthesis
Role of verifiers in ensuring program correctness
• Large Language Models in Program Synthesis
Overview of Language Models (focus on LLMs)
Capabilities and limitations of LLMs in code generation
Interaction between LLMs and verifiers
• Monte Carlo Tree Search (MCTS)
Basics of MCTS and its algorithms
MCTS in the context of program synthesis
Integrating MCTS with LLMs and verifiers for effective tree search
• VerMCTS Architecture
Designing the VerMCTS system
Flow of information between components
Case studies of program synthesis using VerMCTS
• Comparative Analysis
Benchmarks against base language models
Metrics for evaluation: accuracy, time to synthesis, resource usage
Advantages of VerMCTS in different programming tasks
• Practical Implementation
Setting up the environment for VerMCTS
Example project: Synthesizing programs in Dafny
Hands-on exercise: Synthesizing verification conditions in Coq
• Future Directions and Research Opportunities
Potential improvements to the VerMCTS approach
Expanding to other programming languages and paradigms
Discussion on ethical implications and limitations
• Course Conclusion
Summary of key learnings
Open Q&A session for addressing participant doubts
Resources for further study and exploration in AI and program synthesis

Subjects

Computer Science