The paper introduces Selective Supervision for Direct-OPD (S$^2$D-OPD), a refinement of Direct On-Policy Distillation that filters out states where the teacher’s policy change is minimal, as measured by the teacher‑reference Jensen‑Shannon divergence. By masking low‑divergence states and keeping only the top 10% of states per response, S$^2$D-OPD improves held‑out accuracy on AIME and HMMT benchmarks across multiple teacher‑student pairs without additional forward passes.
By Yibo Zhao, Zixuan Yang, Yunshi Lan, Xiang Li
arXiv:2606. 22600v2 Announce Type: replace-cross Abstract: On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers.
By Yan Xie, Sijie Zhu, Tiansheng Wen, Bo Chen, Yifei Wang
The paper introduces Advantage-Based On-Policy Distillation (AOPD), a method that selectively weights teacher actions during online policy distillation by their counterfactual advantage to the student. Unlike standard OPD, which follows the teacher at every state, AOPD reinforces only those teacher actions that improve the student's expected outcome, using an estimator that relies solely on standard student rollouts and terminal rewards. Experiments on math, coding, and Medqa tasks demonstrate that AOPD outperforms both distillation and reinforcement learning baselines in external-teacher and self-distillation settings.
By Mahdi Sabbaghi, George Pappas, Hamed Hassani, Adel Javanmard
arXiv:2607. 04037v1 Announce Type: cross Abstract: On-policy distillation is a powerful way to transfer reasoning ability from a strong teacher to a smaller student: the student samples trajectories from its own policy, and the teacher provides dense token-level supervision on the states the student actually visits.
By Mohammad Sadegh Akhondzadeh, Vijay Lingam, Atula Tejaswi, Chanakya Ekbote, Sujay Sanghavi, Aleksandar Bojchevski
The paper introduces a recursive self-improvement framework for language models that replaces an external teacher with a frozen copy of the student, enabling dynamic co-evolution (DCE) and self-refined concise learning (SRCL). DCE allows the privileged teacher to evolve alongside the student, while SRCL trains on shorter, verified rewrites to reduce verbosity. Experiments show that the combined DCE+SRCL approach outperforms traditional on‑policy self‑distillation across multiple model sizes and math benchmarks, achieving significant accuracy gains and shorter outputs.
By Shangjian Yin, Zehao Zhao, Kavosh Asadi, Rui Liu, Yuchen Lu, Shike Mei, Hang Cui, Luke Simon, Zhouxing Shi, Hamed Firooz
arXiv:2610.07874v1 Announce Type: new
Abstract: On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger...
By Jaehui Hwang, Dongyoon Han, Sangdoo Yun, Byeongho Heo
The paper introduces VISTA, a method that enhances on‑policy self‑distillation (OPSD) by adapting the teacher model toward the student’s distribution using outcome‑verified rollouts. VISTA keeps the standard OPSD student update but selectively adjusts the teacher only on the top‑k positions with the largest teacher‑student KL divergence, without adding new sampling or reward objectives. Experiments on AIME24, AIME25, and HMMT25 with Qwen3 models show that VISTA outperforms OPSD across all scales, improving Avg@12 by up to 2.1 points.
By Zewen Ding, Zezhong Wu, Zhou Tao, Shida Wang, Shizhuo Hou, YongXiang Hua, Haoyu Cao, Linli Xu
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...
By Zhenyu Wang, Tianze Wang, Linjun Zhang, Yifan Hu
arXiv:2610. 13205v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a target policy on its own rollouts using token-level supervision from a stronger teacher, but typically relies on the teacher being substantially more capable than the target.
By Rong Wu, Daocheng Fu, Yu Yang, Xuemeng Yang, Siqi Li, Shu Zou, Yao Zhu, Botian Shi, Bo Zhang, Cong Zhang, Pinlong Cai, Jianbiao Mei
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.
By Anhao Zhao, Haoran Xin, Junlong Tong, Yingqi Fan, Xuan Lu, Ping Nie, Wenjie Li, Xiaoyu Shen
The paper introduces Joint On-Policy Learning and Teaching (JOLT), a method that trains a single policy to act both as a privileged teacher and an unprivileged student. It derives a condition ensuring the teacher’s distillation update aligns positively with the student’s reward gradient, leading to a KL‑regularized teacher objective that matches the student’s current capabilities. Experiments show JOLT improves training efficiency and performance across various tasks, with additional gains when student rewards are incorporated.
By Randy Ardywibowo, Arnav Dalal, Jiantao Jiao
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...