arXiv:2603.00578v2 Announce Type: replace-cross
Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial...
By Jie Cao, Tianwei Lin, Juekai Lin, Zhenxuan Fan, Yunqi Cao, Bo Yuan, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Siliang Tang
BoundaryEvo introduces a two‑stage data‑model co‑evolution framework for distilling long‑chain reasoning from large teacher models into small language models. First, Capability Priming distills a small set of teacher trajectories to give the student an initial reasoning baseline. Then, Capability‑Boundary Co‑Evolution iteratively selects questions the student has not mastered but the teacher can solve, updating the training data as the student’s capability grows. With only 1,000 examples, BoundaryEvo improves Qwen3.5‑4B accuracy from 62.9 % to 75.3 % on IMO‑AnswerBench and from 26.8 % to 46.0 % on AMO‑Bench, outperforming static and dynamic baselines across 11 benchmarks.
By Jiaming Su, Suorong Yang, Xudong Li, Tengchao Yang, Peng Ye
SOD: Step-wise On-policy Distillation for Small Language Model Agents proposes a new framework that adaptively reweights distillation strength at each reasoning step based on step-level divergence. This approach mitigates cascading errors in tool-integrated reasoning by attenuating misleading teacher signals in high-divergence regions while preserving dense guidance where student and teacher align. Experiments on math, science, and code benchmarks show up to 20.86% improvement over the second-best baseline, with a 0.6B student scoring 26.13% on AIME 2025.
By Qiyong Zhong, Mao Zheng, Mingyang Song, Xin Lin, Jie Sun, Houcheng Jiang, Xiang Wang, Junfeng Fang
arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.
By Xiaojun Wu, Xiaoguang Jiang, Huiyang Li, Jucai Zhai, Dengfeng Liu, Qiaobo Hao, Huang Liu, Zhiguo Yang, Ji Xie, Ninglun Gu, Jin Yang, Kailai Zhang, Yelun Bao, Jun Wang
The paper introduces ACTD, an Anchor-Based Cross-Tokenizer Distillation method that aligns vocabularies and sequences to transfer reasoning capabilities from large language models to smaller students. It uses a novel anchor loss with residual regularization to reduce alignment noise and extends the approach to multiple teachers. Experiments on five reasoning benchmarks with three teachers show state‑of‑the‑art results, with the multi‑teacher variant outperforming existing baselines.
By Huiyi Zhang, Zijian Li, Xiaocheng Feng, Weitao Ma, Xiaoliang Yang, Yichong Huang, Bing Qin
The paper introduces Universal Textual Teaching (UTT), a parameter‑update‑free framework that distills knowledge gaps between a Teacher and Student large language model into a reusable natural‑language artifact called a Primer. UTT identifies representative gap cases, then iteratively refines the Primer through a cycle of student attempts, prompter‑generated teaching instructions, teacher demonstrations, and synthesizer consolidation. Experiments on math (Omni‑MATH‑2) and code generation (KernelBench) show significant accuracy gains, outperforming prompt engineering and parameter‑based knowledge distillation, and the Primer generalizes across different Teacher‑Student pairs.
By Zhanyi Lu, Huan Wang