The paper investigates whether on‑policy distillation (OPD) is always advantageous for transferring knowledge from a teacher to a student model. It introduces Semi‑OPD, which uses offline rollouts from the initial student, and demonstrates that across 17 teacher‑student pairs (1.5B–235B parameters) Semi‑OPD outperforms OPD in 14 cases, achieving up to +13.6% accuracy and an 11.4× speedup. The authors show that the benefit of OPD depends on the alignment between teacher and student, measured by an output‑token overlap ratio, and argue that effective distillation requires on‑policy behavior relative to both models.
By Siyan Zhao, Yonggan Fu, Jindong Jiang, Shih-Yang Liu, Song Bian, Byung-Kwan Lee, Sharath Turuvekere Sreenivas, Wenliang Dai, Hanrong Ye, Aditya Grover, Pavlo Molchanov
arXiv:2609.14636v1 Announce Type: new
Abstract: On-policy distillation (OPD) has become a standard approach for transferring capabilities from large teachers to compact students. Its cost, however, i...
By Zhiyu Gui, Kexin Huang, Jia Guo, Junkang Wu, Zihao Wang, Zhiqiang Zhang, Jun Zhou, Jiancan Wu, Xiang Wang
The paper investigates the learning dynamics of on‑policy distillation (OPD), a method that combines teacher supervision with training on student‑generated trajectories. It identifies two stages of the feedback loop: converting the distillation signal into parameter updates, and broadcasting those updates to alter predictions and reshape the rollout distribution. The study explains why students may be unresponsive to teacher disagreement and how updates that improve local agreement can lead to degeneration by increasing the likelihood of poor continuations. It also shows that offline distillation can prepare a student for OPD without boosting immediate accuracy, yet OPD can still yield significant accuracy gains after sufficient offline preparation.
By Guan Zhe Hong, Sumeet Ramesh Motwani, Liang Chen, Yangchen Pan, Junchi Yu, Jialin Yu, Philip Torr, Yi Ren
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:2610.19145v1 Announce Type: new
Abstract: On-policy distillation (OPD) is increasingly studied as a cheaper alternative to reinforcement learning, yet it remains a form of knowledge distillatio...
By Yiping Li, Fan Zhao, Zhibo Hou, Zhiyu An, Wan Du
arXiv:2606. 15912v1 Announce Type: cross Abstract: Multi-turn agents that plan, invoke tools, and interact with environments offer a promising paradigm for solving complex tasks, yet their capabilities typically rely on very large models whose inference cost is prohibitive in practice.
By Gengsheng Li, Mao Zheng, Mingyang Song, Ruiqi Liu, Tianyu Yang, Jie Sun, Qiyong Zhong, Haiyun Guo, Junfeng Fang, Dan Zhang, Jinqiao Wang
On-policy distillation (OPD) is increasingly studied as a cheaper alternative to reinforcement learning, yet it remains a form of knowledge distillation (KD) that relies on teacher supervision. We rev...
arXiv:2609.37510v1 Announce Type: cross
Abstract: On-policy distillation (OPD) trains a student on its own reasoning trajectories using feedback from a stronger teacher. Teacher interventions can imp...
By Yuhao Wang, Ruiyang Ren, Yinan Zhang, Ruiqing Zhang, Jing Liu, Chunyan Miao
The paper investigates on‑policy distillation (OPD), a method that trains a student model using its own rollouts while a teacher remains frozen, to see if it can help a student improve on problems it currently fails. Experiments with Qwen‑3 and Phi‑4 models on a competition‑mathematics benchmark show that OPD actually degrades performance when training focuses on failure cases, even though training loss (KL divergence) continues to drop. However, when OPD is applied to a broader set of problems, it improves accuracy by 4.6 percentage points, indicating that OPD works well when most training data contains correct student prefixes but can reinforce errors if the training regime concentrates on failures.
By Shuo Yang, Zheyu Zhang, Mahito Sugiyama, Gjergji Kasneci
arXiv:2607. 26057v1 Announce Type: cross Abstract: On-policy distillation (OPD) grounds token-level supervision in the student's own trajectory, yet suffers from prefix failure: once the student commits to a wrong reasoning direction, all subsequent generation builds on this deviation, producing misdirected continuations that elicit unreliable supervision and waste compute.
By Haolei Xu, Xiaowen Xu, Haiwen Hong, Zixuan Ni, Hongxing Li, Yiwen Qiu, Weiming Lu, Yongliang Shen
arXiv:2512.22802v2 Announce Type: replace-cross
Abstract: Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remai...
By Amirhossein Tighkhorshid, Zahra Dehghanian, Hamid R. Rabiee
arXiv:2609.36246v1 Announce Type: new
Abstract: We present OLIVE (OnLine InterVEntion). At each iteration, the evolving student policy generates a new prefix, the teacher continues it autoregressivel...
By Haojin Wang, Dylan Zhang, Huaibo Chen, Suhao Yu, Yihang Sun, Zhanyang Jin, Jiaying Ye, Dianqi Li, Prasanna Sattigeri, Kamal Youcef-Toumi, Hao Peng