Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation
Large language models increasingly serve as teachers generating training data for smaller students. Prior multi-teacher knowledge distillation methods merge outputs without determining which frontier model teaches best, often relying on an LLM judge biased toward its own outputs. We introduce a compete-then-collaborate framework where four frontier AI teachers (Claude, Codex-GPT, Grok, Gemini) are ranked head-to-head by an execution-based judge (unit tests and stdin-stdout checks) with fairness
Record details
Published: 9 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Children & education
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (9 July 2026), “Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation,” evidence record 95, https://ethics.ai/record/95 (originally published by arXiv).
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