Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation
Read the original on arXiv Machine Learning →Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation explores how on‑policy distillation (OPD) can be improved by addressing teacher uncertainty that contracts as the teacher continues from a student‑generated prefix. The authors identify Teacher Uncertainty Contraction (TUC) and theoretically analyze its variance‑bias trade‑off, leading to the proposal of Adaptive‑Continuations On‑Policy Distillation (AC‑OPD). Experiments on mathematical reasoning and code generation show that AC‑OPD consistently outperforms standard OPD, with controlled‑continuation and matched‑budget analyses supporting the adaptive‑continuation design.
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