The paper investigates how much training data is required for on‑policy distillation (OPD) of foundation models. It shows that in two single‑teacher settings, only eight real prompts can match the performance of 17,000 problems, and that 64 self‑generated questions can replace full external datasets. In multi‑teacher scenarios, 1,000 generated questions achieve comparable results to 7,000 real examples, while fully implicit question generation works only in limited cases.
By Gengsheng Li, Mao Zheng, Mingyang Song, Jie Sun, Zeyuan Liu, Ruiqi Liu, Tianyu Yang, Qiyong Zhong, Haiyun Guo, Junfeng Fang, Shiming Xiang, Jinqiao Wang, Tat-Seng Chua
arXiv:2607. 02502v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as both the teacher and the student with different levels of information access.
By Yunhe Li, Hao Shi, Wenhao Liu, Mengzhe Ruan, Hanxu Hou, Zhongxiang Dai, Shuang Qiu, Linqi Song
arXiv:2609.14193v1 Announce Type: cross
Abstract: On-policy distillation (OPD) has become a standard component of frontier post-training pipelines, yet how much its training data actually contributes...
By Gengsheng Li, Mao Zheng, Mingyang Song, Jie Sun, Zeyuan Liu, Ruiqi Liu, Qiyong Zhong, Haiyun Guo, Junfeng Fang, Jinqiao Wang
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: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:2607. 02234v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-level supervision on the student's own generated trajectories.
By Zhanming Shen, Jintao Tong, Shaotian Yan, Chen Shen, Hao Chen, Wentao Ye, Xiaomeng Hu, Rui Miao, Haobo Wang, Junbo Zhao, Gang Chen, Jieping Ye
arXiv:2609.36546v1 Announce Type: cross
Abstract: On-policy distillation (OPD) trains a student model on its self-generated trajectories with dense token-level teacher feedback. However, naive OPD ma...
By Shutong Wu, Xiwen Chen, Brendan Rappazzo, Daiheng Zhang, Anderson Schneider, Yuriy Nevmyvaka, Jiawei Zhang
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...
CoTranslator is a method that converts reverse chain-of-thought (CoT) responses into verified forward CoT responses, enabling efficient large-scale generation and verification of training data for reasoning distillation. Trained on a small set of paired reverse and rejection-sampled forward responses, it improves student model accuracy on mathematics benchmarks by up to 5.82 percentage points and retains 95.0% of rejection sampling’s accuracy while using only 35.9% of its compute. The translator also transfers to new datasets, teachers, and STEM problems, boosting average STEM accuracy for 4B and 8B students by 4.88 and 4.31 percentage points, respectively.
By Zinan Tang, Zhuoshi Pan, Kairui Chen, Ze Yan, Shaomian Zheng, Yukun Zhang, Gang Wang, Biqing Huang
The paper introduces a method for offline on‑policy distillation that addresses the problem of imperfect teacher supervision. By training on teacher‑successful problems and measuring changes in token likelihoods on teacher‑failed trajectories, the authors derive a learnability signal that weights the distillation loss. This approach improves performance on mathematical reasoning and code generation tasks while reducing computational cost compared to online distillation.
By Yihao Ai, Weilong Yan
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
arXiv:2602. 22495v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such models motivates distillation into smaller students.
By Zhaoyang Zhang, Shuli Jiang, Yantao Shen, Yuting Zhang, Dhananjay Ram, Shuo Yang, Zhuowen Tu, Wei Xia, Stefano Soatto