Language models can generate programming exercises and solutions, but incorrect answers, weak tests, and repetitive tasks can limit their value as training data. This thesis builds on CodifieRL to generate synthetic coding tasks from real repositories and investigate whether execution-based filtering improves their quality and usefulness for training code models.