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When to Follow the Teacher: Advantage-Based On-Policy Distillation

Mahdi Sabbaghi, George Pappas, Hamed Hassani, Adel Javanmard · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31563 · open-access PDF

Arabic

في كثير من الأحيان ، يمتاز سياسات الإنترنت (OPD) عن طريق التدريب على الطرق المختلطة المختلطة (SFT) عن طريق التدريب على الطرق المختلطة المختلطة مباشرة من الطلاب ، ومع ذلك ، يمتاز سياسات الإنترنت المختلطة عن طريق التدريب الطلاب على اتباع المعلم في كل حالة من الطرق ، مع التأكيد على أن توجيه المعلم دائمًا مفيدًا. بدلاً من ذلك ، نحن نختلف ما يفضل المعلم عن ما يساعد الطالب الحالي على النجاح.

Machine translation (Meta M2M100); the English original follows.

Abstract

Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student. However, standard OPD trains the student to follow the teacher at every state of the trajectory, assuming that teacher guidance is always beneficial. We instead distinguish between what the teacher prefers and what actually helps the current student succeed. We introduce Advantage-Based On-Policy Distillation (AOPD), which weights teacher actions by their counterfactual advantage under the student. Teacher actions that improve th

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