Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
Fuente:
arXiv
Saved in:
| Main Authors: | Cai, Wenrui, Wang, Chengyu, Yan, Junbing, Huang, Jun, Fang, Xiangzhong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models
by: Wang, Chengyu, et al.
Published: (2025)
by: Wang, Chengyu, et al.
Published: (2025)
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
by: Cai, Wenrui, et al.
Published: (2025)
by: Cai, Wenrui, et al.
Published: (2025)
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
by: Cai, Wenrui, et al.
Published: (2025)
by: Cai, Wenrui, et al.
Published: (2025)
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
by: Wang, Chengyu, et al.
Published: (2025)
by: Wang, Chengyu, et al.
Published: (2025)
Do Large Language Models Understand Logic or Just Mimick Context?
by: Yan, Junbing, et al.
Published: (2024)
by: Yan, Junbing, et al.
Published: (2024)
From Correction to Mastery: Reinforced Distillation of Large Language Model Agents
by: Lyu, Yuanjie, et al.
Published: (2025)
by: Lyu, Yuanjie, et al.
Published: (2025)
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
by: Paliotta, Daniele, et al.
Published: (2025)
by: Paliotta, Daniele, et al.
Published: (2025)
SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training
by: Tang, Shengkun, et al.
Published: (2026)
by: Tang, Shengkun, et al.
Published: (2026)
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
by: Zhang, Yudi, et al.
Published: (2025)
by: Zhang, Yudi, et al.
Published: (2025)
Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation
by: Chen, Wei-Rui, et al.
Published: (2025)
by: Chen, Wei-Rui, et al.
Published: (2025)
AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use
by: Lyu, Yuanjie, et al.
Published: (2026)
by: Lyu, Yuanjie, et al.
Published: (2026)
Enhancing Reasoning Capabilities in SLMs with Reward Guided Dataset Distillation
by: Padarha, Shreyansh
Published: (2025)
by: Padarha, Shreyansh
Published: (2025)
A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
by: Wang, Chengyu, et al.
Published: (2025)
by: Wang, Chengyu, et al.
Published: (2025)
Distilling Mathematical Reasoning Capabilities into Small Language Models
by: Zhu, Xunyu, et al.
Published: (2024)
by: Zhu, Xunyu, et al.
Published: (2024)
Detecting Distillation Data from Reasoning Models
by: Zhang, Hengxiang, et al.
Published: (2025)
by: Zhang, Hengxiang, et al.
Published: (2025)
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward
by: Li, Dongheng, et al.
Published: (2024)
by: Li, Dongheng, et al.
Published: (2024)
Mixed Distillation Helps Smaller Language Model Better Reasoning
by: Li, Chenglin, et al.
Published: (2023)
by: Li, Chenglin, et al.
Published: (2023)
Skill-Conditioned Gated Self-Distillation for LLM Reasoning
by: Huang, Jiazhen, et al.
Published: (2026)
by: Huang, Jiazhen, et al.
Published: (2026)
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
by: Zhu, Xunyu, et al.
Published: (2024)
by: Zhu, Xunyu, et al.
Published: (2024)
Hide to See: Reasoning-prefix Masking for Visual-anchored Thinking in VLM Distillation
by: Yu, Seonghoon, et al.
Published: (2026)
by: Yu, Seonghoon, et al.
Published: (2026)
Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs
by: Xiao, Yilin, et al.
Published: (2025)
by: Xiao, Yilin, et al.
Published: (2025)
Controlling Thinking Speed in Reasoning Models
by: Lin, Zhengkai, et al.
Published: (2025)
by: Lin, Zhengkai, et al.
Published: (2025)
Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effort
by: Wang, Xinpeng, et al.
Published: (2025)
by: Wang, Xinpeng, et al.
Published: (2025)
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
by: Wang, Wei, et al.
Published: (2024)
by: Wang, Wei, et al.
Published: (2024)
Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation
by: Zhu, Xunyu, et al.
Published: (2024)
by: Zhu, Xunyu, et al.
Published: (2024)
Backtracking When It Strays: Mitigating Dual Exposure Biases in LLM Reasoning Distillation
by: Wang, Bing, et al.
Published: (2026)
by: Wang, Bing, et al.
Published: (2026)
Qwen2 Technical Report
by: Yang, An, et al.
Published: (2024)
by: Yang, An, et al.
Published: (2024)
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
by: Phan, Phuc, et al.
Published: (2024)
by: Phan, Phuc, et al.
Published: (2024)
ThinkTuning: Instilling Cognitive Reflections without Distillation
by: RRV, Aswin, et al.
Published: (2025)
by: RRV, Aswin, et al.
Published: (2025)
Style over Substance: Distilled Language Models Reason Via Stylistic Replication
by: Lippmann, Philip, et al.
Published: (2025)
by: Lippmann, Philip, et al.
Published: (2025)
Process Reward Models That Think
by: Khalifa, Muhammad, et al.
Published: (2025)
by: Khalifa, Muhammad, et al.
Published: (2025)
EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
by: Lin, Liang, et al.
Published: (2026)
by: Lin, Liang, et al.
Published: (2026)
Exploring Reasoning Reward Model for Agents
by: Fan, Kaixuan, et al.
Published: (2026)
by: Fan, Kaixuan, et al.
Published: (2026)
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
by: Zhou, Yuhang, et al.
Published: (2024)
by: Zhou, Yuhang, et al.
Published: (2024)
MixReasoning: Switching Modes to Think
by: Lu, Haiquan, et al.
Published: (2025)
by: Lu, Haiquan, et al.
Published: (2025)
MiniLLM: On-Policy Distillation of Large Language Models
by: Gu, Yuxian, et al.
Published: (2023)
by: Gu, Yuxian, et al.
Published: (2023)
Chunk-Distilled Language Modeling
by: Li, Yanhong, et al.
Published: (2024)
by: Li, Yanhong, et al.
Published: (2024)
Distill-C: Enhanced NL2SQL via Distilled Customization with LLMs
by: Hoang, Cong Duy Vu, et al.
Published: (2025)
by: Hoang, Cong Duy Vu, et al.
Published: (2025)
SOD: Step-wise On-policy Distillation for Small Language Model Agents
by: Zhong, Qiyong, et al.
Published: (2026)
by: Zhong, Qiyong, et al.
Published: (2026)
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
by: Yang, Chuanpeng, et al.
Published: (2024)
by: Yang, Chuanpeng, et al.
Published: (2024)
Similar Items
-
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models
by: Wang, Chengyu, et al.
Published: (2025) -
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
by: Cai, Wenrui, et al.
Published: (2025) -
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
by: Cai, Wenrui, et al.
Published: (2025) -
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
by: Wang, Chengyu, et al.
Published: (2025) -
Do Large Language Models Understand Logic or Just Mimick Context?
by: Yan, Junbing, et al.
Published: (2024)