Think Before You Prune: Selective Self-Generated Calibration for Pruning Large Reasoning Models
Fuente:
arXiv
Saved in:
| Main Authors: | Xiang, Yang, Ji, Yixin, Li, Juntao, Zhang, Min |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
by: Wang, Ziyan, et al.
Published: (2025)
by: Wang, Ziyan, et al.
Published: (2025)
Beware of Calibration Data for Pruning Large Language Models
by: Ji, Yixin, et al.
Published: (2024)
by: Ji, Yixin, et al.
Published: (2024)
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
by: Xiang, Yang, et al.
Published: (2026)
by: Xiang, Yang, et al.
Published: (2026)
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
by: Hou, Bairu, et al.
Published: (2025)
by: Hou, Bairu, et al.
Published: (2025)
Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR
by: Xu, Haobo, et al.
Published: (2026)
by: Xu, Haobo, et al.
Published: (2026)
IG-Pruning: Input-Guided Block Pruning for Large Language Models
by: Qiao, Kangyu, et al.
Published: (2025)
by: Qiao, Kangyu, et al.
Published: (2025)
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
by: Wu, Guangxin, et al.
Published: (2026)
by: Wu, Guangxin, et al.
Published: (2026)
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning
by: Xu, Ruotao, et al.
Published: (2026)
by: Xu, Ruotao, et al.
Published: (2026)
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
by: Guo, Hongcheng, et al.
Published: (2025)
by: Guo, Hongcheng, et al.
Published: (2025)
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
by: Sengupta, Ayan, et al.
Published: (2025)
by: Sengupta, Ayan, et al.
Published: (2025)
Entropy-Based Block Pruning for Efficient Large Language Models
by: Yang, Liangwei, et al.
Published: (2025)
by: Yang, Liangwei, et al.
Published: (2025)
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
by: Bandari, Abhinav, et al.
Published: (2024)
by: Bandari, Abhinav, et al.
Published: (2024)
NutePrune: Efficient Progressive Pruning with Numerous Teachers for Large Language Models
by: Li, Shengrui, et al.
Published: (2024)
by: Li, Shengrui, et al.
Published: (2024)
Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning
by: Wang, Jianing, et al.
Published: (2025)
by: Wang, Jianing, et al.
Published: (2025)
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
by: Jiang, Titong, et al.
Published: (2025)
by: Jiang, Titong, et al.
Published: (2025)
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models
by: Jiang, Yuxuan, et al.
Published: (2025)
by: Jiang, Yuxuan, et al.
Published: (2025)
Think Clearly: Improving Reasoning via Redundant Token Pruning
by: Choi, Daewon, et al.
Published: (2025)
by: Choi, Daewon, et al.
Published: (2025)
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality
by: Yu, Byeongho, et al.
Published: (2025)
by: Yu, Byeongho, et al.
Published: (2025)
Think Before You Lie: How Reasoning Leads to Honesty
by: Yuan, Ann, et al.
Published: (2026)
by: Yuan, Ann, et al.
Published: (2026)
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization
by: Deng, Hexuan, et al.
Published: (2024)
by: Deng, Hexuan, et al.
Published: (2024)
Pruning General Large Language Models into Customized Expert Models
by: Zhao, Yirao, et al.
Published: (2025)
by: Zhao, Yirao, et al.
Published: (2025)
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
by: Du, Chengyu, et al.
Published: (2024)
by: Du, Chengyu, et al.
Published: (2024)
Think Only When You Need with Large Hybrid-Reasoning Models
by: Jiang, Lingjie, et al.
Published: (2025)
by: Jiang, Lingjie, et al.
Published: (2025)
PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
by: He, Bowei, et al.
Published: (2025)
by: He, Bowei, et al.
Published: (2025)
Self-calibration for Language Model Quantization and Pruning
by: Williams, Miles, et al.
Published: (2024)
by: Williams, Miles, et al.
Published: (2024)
PruneTIR: Inference-Time Tool Call Pruning for Effective yet Efficient Tool-Integrated Reasoning
by: Zhang, Luan, et al.
Published: (2026)
by: Zhang, Luan, et al.
Published: (2026)
Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation
by: Chen, Xinrui, et al.
Published: (2025)
by: Chen, Xinrui, et al.
Published: (2025)
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
by: Zhang, Qingjie, et al.
Published: (2025)
by: Zhang, Qingjie, et al.
Published: (2025)
DLP: Dynamic Layerwise Pruning in Large Language Models
by: Chen, Yuli, et al.
Published: (2025)
by: Chen, Yuli, et al.
Published: (2025)
On the Impact of Calibration Data in Post-training Quantization and Pruning
by: Williams, Miles, et al.
Published: (2023)
by: Williams, Miles, et al.
Published: (2023)
Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection
by: Jana, Soumyadeep, et al.
Published: (2025)
by: Jana, Soumyadeep, et al.
Published: (2025)
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning
by: Huang, Xijie, et al.
Published: (2023)
by: Huang, Xijie, et al.
Published: (2023)
Instruction-Following Pruning for Large Language Models
by: Hou, Bairu, et al.
Published: (2025)
by: Hou, Bairu, et al.
Published: (2025)
High-Fidelity Pruning for Large Language Models
by: Zhu, Yijun, et al.
Published: (2026)
by: Zhu, Yijun, et al.
Published: (2026)
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
by: Ji, Yixin, et al.
Published: (2024)
by: Ji, Yixin, et al.
Published: (2024)
PAT: Pruning-Aware Tuning for Large Language Models
by: Liu, Yijiang, et al.
Published: (2024)
by: Liu, Yijiang, et al.
Published: (2024)
GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching
by: Su, Guinan, et al.
Published: (2025)
by: Su, Guinan, et al.
Published: (2025)
Self-Data Distillation for Recovering Quality in Pruned Large Language Models
by: Thangarasa, Vithursan, et al.
Published: (2024)
by: Thangarasa, Vithursan, et al.
Published: (2024)
Large Language Model Pruning
by: Huang, Hanjuan, et al.
Published: (2024)
by: Huang, Hanjuan, et al.
Published: (2024)
Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
by: He, Jiashu, et al.
Published: (2026)
by: He, Jiashu, et al.
Published: (2026)
Similar Items
-
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
by: Wang, Ziyan, et al.
Published: (2025) -
Beware of Calibration Data for Pruning Large Language Models
by: Ji, Yixin, et al.
Published: (2024) -
When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
by: Xiang, Yang, et al.
Published: (2026) -
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
by: Hou, Bairu, et al.
Published: (2025) -
Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR
by: Xu, Haobo, et al.
Published: (2026)