How We Built Zeta2: Training an Edit Prediction Model in Production
Zed trained Zeta2 via production edit data distillation: using frontier models to generate candidate edits, filtering low-quality outputs with static evaluation and a 'repair' mechanism, yielding ~100K high-quality training examples; the entire pipeline is JSONL-based for fast experimentation.
入选理由:Zeta2 使用 distillation + repair 两阶段流程:先由 frontier model 生成编辑预测,再用启发式规则检测失败案例并触发二次修正


