Test‑time policy optimization replaces supervised labels
This story is from 2026-09-03. It is preserved in the archive; the latest stories are on the live feed.
An asymmetric test‑time objective lets on‑policy distillation hit supervised OPSD performance without any hand‑labeled data. By rewarding rollouts that agree with a teacher and penalizing those that diverge, the method turns unlabeled interaction streams into a reliable supervisory signal. The resu…
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- 2026-09-03 05:00 · DEV Community — Machine Learning
Test‑time policy optimization replaces supervised labels