How We Built Zeta2: Training an Edit Prediction Model in Production — Ben Kunkle, Zed
May 30, 2026 · 10:50
Ben Kunkle, edit predictions lead at Zed, explains how they built Zeta2, a small specialized model for edit prediction in production. The pipeline pulls opt-in production edit traces, distills them through a frontier teacher, and routes bad predictions through a repair step before formatting for the student. To validate settled data, Zed originally ran 10 frontier model predictions per example and measured Levenshtein distance to the final state, but for 100,000 training examples that is a million frontier model requests — prohibitively expensive. The fix: Zeta2's student model now approaches teacher quality, so they run it 50 times instead at negligible cost. Ideal training examples sit in the middle of the Levenshtein distance distribution: too close to the settled state is obvious, too far is noise. A metric called reversal ratio — how often the model undoes exactly what the user just typed — was the key diagnostic for catching bad model behavior before shipping.
