Reasoning Distillation

Follow this topic Ask about this topic

Momentum

3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 14

All topics

Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Oct 7, 2026Taehoon Kim, Seunggeun Cho, Dongsu HanReasoning Distillation LLM Reasoning
KAIST
Solving Without Stopping: On-Policy Distillation at Small Scale
Sep 29, 2026Hongyang Li, Yiming Zhu, Xiao Li +3Reasoning Distillation Small Language Models
University of Luxembourg · Seafill Open-Source Community · Universit´e Paris-Saclay
ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning
Sep 27, 2026Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song +2Reasoning Distillation Multi-Teacher Knowledge Distillation
School of Data Science, The Chinese University of Hong Kong, Shenzhen · Shanghai Artificial Intelligence Laboratory
TeacherGRPO: Closing the Capacity Gap in Reasoning Distillation via Teacher Alignment
Sep 27, 2026Zhenyu Lei, Zihan Chen, Yaochen Zhu +5Reasoning Distillation RL for Language Model Reasoning
University of Virginia · Netflix · University of Washington +2
Benchmarking and Reasoning Distillation of Large Language Models for Feedback Controller Design in Complex Dynamical Systems
Aug 7, 2026Zhongchao Zhou, Yixuan Xie, Wenwei Yu +5LLM Evaluation Language Model-Based Control
Matsuo-Iwasawa Laboratory, Department of Technology Management for Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan · Department of Statistics, University of Illinois Urbana-Champaign, Champaign, IL, USA · Center for Frontier Medical Engineering, Chiba University, Chiba, Japan +1
Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks
Aug 1, 2026Faizan Faisal, Prem Devanbu, Toufique AhmedReasoning Distillation LLM Prompting
University of California, Davis · IBM
DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
Jul 7, 2026He Liu, Changtao Miao, Xinjie Yang +12Reasoning Distillation LLM Guardrails
Ant Digital Technologies, Ant Group
Z-Reward: Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions
Jun 8, 2026Xin Jin, Huanqia Cai, Zhen Li +9Reward Modeling Reasoning Distillation
Z-Image Team, Alibaba Group · VCIP, CS, Nankai University
Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility
May 28, 2026Jiahao Huang, Fei Cheng, Junfeng Jiang +1Reasoning Distillation Training Data Selection
University of Tokyo · Kyoto University · National Institute of Informatics
Decoupling KL and Trajectories: A Unified Perspective for SFT, DAgger, Offline RL, and OPD in LLM Distillation
May 16, 2026Anhao Zhao, Haoran Xin, Yingqi Fan +3Reasoning Distillation On-Policy Distillation
Eastern Institute of Technology, Ningbo · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) · Shanghai Jiao Tong University +1
Curriculum Learning-Guided Progressive Distillation in Large Language Models
May 11, 2026Jincheng Cao, Fanzhi Zeng, Leqi Liu +1Reasoning Distillation Language Model Distillation
The University of Texas at Austin · Google Research
ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
Apr 21, 2026Kunquan Li, Yingxue Zhang, Fandong Meng +1Reasoning Distillation Machine Translation
School of Informatics, Xiamen University, China · WeChat AI, Tencent Inc, China
Revisiting the Capacity Gap in Chain-of-Thought Distillation from a Practical Perspective
Apr 10, 2026Tokio Kajitsuka, Ukyo Honda, Sho TakaseCoT Distillation Reasoning Distillation
The University of Tokyo, Tokyo, Japan · CyberAgent, Tokyo, Japan
Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains
Apr 9, 2026Haokai Ma, Lee Yan Zhen, Gang Yang +4RL for Language Models Reinforcement Learning
National University of Singapore, Singapore · Singapore Management University, Singapore

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation

Taehoon Kim, Seunggeun Cho, Dongsu Han

KAIST

Reasoning Distillation LLM Reasoning Multi-Teacher Knowledge Distillation Process Supervision

Open paper AskPDF arXiv

Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reasoning to smaller models. Standard distillation methods rely on outcome-based rewards, failing to distinguish between sound reasoning and lucky guesses. We propose Collaborative Reasoning Distillation (CRD), a framework that enhances reasoning in compact models through three innovations: (1) interactive cross-feedback where teachers iteratively critique each other's reasoning, (2) fine-grained step-wise quality assessment capturing logical validity independent of final answers, and (3) coherence-aware step stitching that synthesizes complementary strengths. Students are trained via Reasoning Quality Optimization (RQO) with budget constraints. Our model, CRD-4B, achieves 97.3% on MATH-500 and 70.3% on AIME'25, surpassing baselines while using only 50K training examples, up to 12 times smaller than the datasets of comparable models.

Momentum

Related topics

Latest papers 14