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OC-OPD: Observation-coordinated Supervision for On-Policy Distillation — arXiv:2611.01965

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01965
License (as archived): CC0-1.0
Archived: 2026-11-03T11:07:20.036Z
SHA-256 of the archived text: 2942b4b27743c4a5d4bf6edbaf333fb980b8fb8edb26f254dcfed5074e637f6b
Stable URL of this page: https://metraipolis.com/documento/dc_ec16cd0a3dd2

How to cite

OC-OPD: Observation-coordinated Supervision for On-Policy Distillation — arXiv:2611.01965 — arXiv (en), https://arxiv.org/abs/2611.01965 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec16cd0a3dd2 (Metraipolis archive dc_ec16cd0a3dd2, sha256 2942b4b27743c4a5d4bf6edbaf333fb980b8fb8edb26f254dcfed5074e637f6b)

BibTeX

@misc{dcec16cd0a3dd2,
  title = {OC-OPD: Observation-coordinated Supervision for On-Policy Distillation — arXiv:2611.01965},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01965},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec16cd0a3dd2; sha256 2942b4b27743c4a5d4bf6edbaf333fb980b8fb8edb26f254dcfed5074e637f6b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01965. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reverse and forward Kullback--Leibler (KL) divergences provide complementary supervision in on-policy distillation: reverse KL refines the student's active token rankings, and forward KL allocates mass to teacher-supported candidates. At student-generated prefixes, we form a four-observation matrix by treating the student and teacher as both candidate sources and evaluators. The diagonal self-observations measure uncertainty, while source-centered cross-observations recover the two KL objectives. We establish an exact identity equating the Fisher inverse-metric coupling of their logit gradients with the sum of these contrasts, yielding a common local descent direction for distinct positive distributions. Observation-coordinated on-policy distillation (OC-OPD) uses observations to set preferences, closed-form bargaining to coordinate gradients, and distributional discrepancy to scale the update. Across three heterogeneous student settings (non-thinking generation, thinking generation, and base-model initialization), OC-OPD achieves strong gains in mathematical reasoning and transfers effectively to out-of-distribution scientific question answering and code generation. Our code is publicly available at: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2611.01965 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:46.061Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).