arXiv:2608.16647v2 Announce Type: replace Abstract: On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalizati...
On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a controlled study that varies one generalization factor at a time, from in-domain distribution shifts to cross-domain transfer and the multi-teacher setting.
arXiv:2608. 08176v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves the reasoning abilities of LLMs by internalizing privileged context into model parameters through self-distillation.
arXiv:2607. 26246v1 Announce Type: new Abstract: On-policy distillation (OPD), which aligns a student with the teacher's token-level distribution on the student's own rollouts, is an effective paradigm for transferring capabilities across LLMs.
On-policy Distillation (OPD) supervises a student model on trajectories sampled from its own policy by minimizing the divergence between the output distributions of the teacher and student at each token position, thereby providing dense token-level supervision. Although existing OPD methods have demonstrated strong performance in improving the reasoning ability of student models, their objectives fundamentally rely on token-level distribution matching.
arXiv:2609.37044v1 Announce Type: new Abstract: Explicit intermediate reasoning gives large language models (LLMs) a stronger problem-solving mode. We study learning from this think-mode advantage vi...
arXiv:2607. 27770v1 Announce Type: new Abstract: A single reinforcement-learning run can produce a strong reasoner yet an incomplete teacher: it often amplifies only a subset of the valid solution modes.
arXiv:2609.38025v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OP...
MOPD‑Router rethinks teacher routing in multi‑teacher on‑policy distillation by routing supervision over the full teacher pool at each token, eliminating the need for prompt‑level domain labels or a separate routing model. The framework offers a plug‑in interface for various metrics, and introduces ExpertAlign, which scores teachers based on how well their corrections reflect their specialized post‑training knowledge. Experiments on both unlabeled and domain‑labeled mixtures show that ExpertAlign outperforms existing methods, improving overall scores by up to 12.3% on unlabeled data and 7.8% on domain‑labeled data. whyItMatters":"Token‑level routing enables the use of complementary supervision across domains without relying on domain labels, leading to significant performance gains in multi‑teacher distillation settings."
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD treats all teacher signals equally, assuming that...
The paper introduces DIAL-OPD, a token‑selection strategy for on‑policy distillation that weights reward magnitude by the logarithmic mean of teacher and student probabilities, controlled by a parameter β. Experiments on four teacher‑student pairs across seven mathematical reasoning benchmarks show that retaining only 40 % of tokens with DIAL‑OPD yields up to 5.25 % higher mean accuracy than vanilla OPD and doubles AIME25 Pass@16, outperforming even stronger full‑token baselines. The study demonstrates that careful supervision allocation can surpass gains from simply scaling teacher size.
On-policy self-distillation (OPSD) improves reasoning by training a problem-only student on its own rollouts using dense token-level supervision from a privileged teacher that also sees a reference so...