CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation

Understand

The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source.

• However, while competitive programming platforms provide abundant problems and solutions, high-quality test cases for verification remain scarce.
• Existing approaches attempt to synthesize test cases using Large Language Models (LLMs), but rely solely on the model's intrinsic generation capabilities without external feedback, frequently resulting in insufficiently diverse cases.
• To address this limitation, we propose a $\textbf{Feedback-Driven Iterative Framework}$ for comprehensive test case construction.