Scaling Code Generation Without Expensive Teachers
A new approach for creating synthetic programming data at scale
Case2Code introduces a novel method to synthesize high-quality training data for code generation models without relying on expensive teacher LLMs.
- Creates code samples by starting with test cases and inferring the programs that would satisfy them
- Generates diverse, correct programming solutions across multiple languages
- Produces scalable training data at significantly lower cost than teacher-based approaches
- Demonstrates improved performance on code generation benchmarks
For education, this research enables more accessible development of code tutoring systems and programming assistants that can help students learn coding concepts through customized examples and solutions.