Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation

Jul 10, 2026

A new study presents a compete-then-collaborate framework in which four frontier AI teachers (Claude, Codex-GPT, Grok, Gemini) are ranked using execution-based tests and then collaborate to build a verifiable curriculum for a student model. The authors find that imitation learning on verified solutions does not improve and can even degrade student performance, while using the curriculum as a reinforcement learning environment yields a 49% relative gain on competition problems. The results suggest that AI-teacher collaboration is most valuable for constructing verifiable environments rather than pooling answers for imitation.

Why it matters: This research challenges the prevailing approach of using frontier models to generate training data for smaller models, showing that imitation can be counterproductive and that reinforcement learning with verifiable rewards is more effective.

Full story at: arXiv AI/ML ↗