Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation

Understand

Recent advances in large language models (LLMs) have improved their performance on coding benchmarks.

• However, improvement is plateauing due to the exhaustion of readily available high-quality data.
• Prior work has shown the potential of synthetic self-instruct data, but naively training on a model's own outputs can cause error accumulation, especially in coding tasks, where generalization may collapse due to overly simple or erroneous training data, highlighting the need for rigorous quality checks on synthetic data.
• In this work, we explore an effective approach whereby the model itself verifies the correctness of its own data.