arXiv:2605. 26872v2 Announce Type: replace-cross Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations.
By Zhengyu Hu, Zheyuan Xiao, Linxin Song, Fengqing Jiang, Yuetai Li, Zhengyu Chen, Zhihan Xiong, Yue Liu, Junhao Lin, Yao Su, Lijie Hu, Kaize Ding, Teng Xiao, Radha Poovendran
arXiv:2605.26872v4 Announce Type: replace-cross
Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations....
By Zhengyu Hu, Zheyuan Xiao, Linxin Song, Fengqing Jiang, Yuetai Li, Zhihan Xiong, Yue Liu, Junhao Lin, Yao Su, Lijie Hu, Kaize Ding, Teng Xiao, Radha Poovendran
arXiv:2607. 05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to this task.
By Haz Sameen Shahgir, Yufei Li, Frank Shyu, Luke Simon, Sandeep Pandey, Xi Liu, Yue Dong
Stepwise-Querying is a method that extracts step-by-step explanations from a black-box teacher model without needing its internal chain-of-thought. By independently querying the teacher for intermediate derivations, validity checks, and corrections for each step, the gathered explanations are used to fine-tune smaller student models. Experiments on mathematics and code benchmarks show that this approach outperforms three baselines in 14 of 16 comparisons, and the introduced Stepwise Explanation Information Gain metric helps diagnose the usefulness of intermediate reasoning.
By Dicong Yu, Wenhua Wang, Tian Wang
arXiv:2610.02703v1 Announce Type: new
Abstract: On-policy distillation (OPD) improves large language model reasoning by training students on their own rollouts with dense token-wise supervision from...
By Yuxiang Zhang, Ding Cao, Shuting Cui, Lei Wang, Weijieying Ren, Tianxiang Zhao
The paper studies how on‑policy distillation (OPD) can improve answer accuracy while reducing a model’s reliance on supporting evidence. Through matched document interventions, the authors identify an "evidence bypass" phenomenon where a student model keeps an originally correct answer even after the supporting document is changed. They propose Evidence‑Paired OPD (EP‑OPD), which uses matched documents and an answer‑level margin to recover most of the counterfactual accuracy lost by standard OPD while preserving much of its original‑document accuracy gains.
By Ruoran Xu, Yilun Zhao, Ziyu Chen, Xiaoqiang kang, Arman Cohan