Reference solutions and question-specific rubrics are not always available to guide the teacher in self-distillation. A large language model's own solution attempts offer an alternative, but their quality is difficult to assess in open-ended tasks without automatic correctness checks, and even correct attempts can involve uncertain token predictions. We propose Reliability--Uncertainty Self-Distillation (\method{}) to learn from these attempts. The same model serves as teacher and student: the teacher receives the question and completed attempt, and the student matches the teacher's token-level predictions using a question-only prompt. \method{} weights each attempt's distillation loss by additively combining judged reliability, estimated from a self-judge's score and confidence, with student predictive uncertainty. Across eleven reasoning benchmarks, \method{} outperforms all five baselines in average accuracy, reaching 70.47\% on Qwen3-4B and 76.69\% on Qwen3-8B (8.90 and 2.10 percentage points above the base models). Data and code are available at https://github.com/JP-25/RUSD.
Reasoning Compression with Mixed-Policy Distillation
Technical University of Munich（慕尼黑技术大学）；GESIS – Leibniz Institute for the Social Sciences（莱布尼茨社会科学研究所）；Northeastern University（东北大学）；LMU Munich（慕尼黑大学）；Siemens AG（西门子股份公司）；University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Mina AI
Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation
Beihang University（北航）；Heilongjiang University（黑龙江大学）；Beijing University of Civil Engineering and Architecture（北京建筑大学）；Yanshan University（燕山大学）；Shandong University（山东大学）；Renmin University of China（中国人民大学）
Learning to Revise Reasoning with Segment-wise On-Policy Distillation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Science and Technology of China（中国科学技术大学）；Alibaba Group（阿里巴巴集团）；Stanford University（斯坦福大学）
Hán Dān Xué Bù (Mimicry) or Qīng Chū Yú Lán (Mastery)? A Cognitive Perspective on Reasoning Distillation in Large Language Models
School of Philosophy, Anhui University（安徽大学哲学学院）；Institute of Neuroscience, Chinese Academy of Sciences（中国科学院神经科学研究所）；Faculty of Education, University of Cambridge（剑桥大学教育学院）；School of Foreign Studies, South China Normal University（华南师范大学外语学院）；Division of Psychology and Language Sciences, University College London（伦敦大学学院心理学与语言科学系）
Backtracking When It Strays: Mitigating Dual Exposure Biases in LLM Reasoning Distillation
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Key Laboratory of Symbolic Computation and Knowledge Engineering, MoE, Jilin University（吉林大学符号计算与知识工程重点实验室）；Tongyi Lab, Alibaba Group（阿里集团通义实验室）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
School of Computer Science and Engineering, Northeastern University, China（东北大学计算机科学与工程学院）；Tsinghua University（清华大学）；Meituan（美团）；Meta AI；NiuTrans Research, Shenyang, China（新译研究院，沈阳，中国）
Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs Distillation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
