OBC‑Prune introduces an outcome‑based calibration approach for pruning large reasoning models, focusing on the causal importance of each reasoning sentence rather than uniform activation salience. By pairing correct and incorrect rollouts and using intervention‑based analysis, it assigns per‑token weights that guide one‑shot pruning methods such as SparseGPT, Wanda, and ALPS. Experiments on DeepSeek‑R1‑Distill‑Qwen models show consistent accuracy gains and shorter reasoning traces across multiple benchmarks at 40‑50% sparsity.
By Ha Lan Nguyen, Huy Hoang Tran, Trac-Duy Tran, Dung D. Le
The paper revisits the impact of pruning on large language models (LLMs) during test-time scaling (TTS). While prior work found that structured pruning degrades reasoning performance, this study shows that unstructured pruning—removing only specific redundant weights—can actually improve TTS performance on reasoning benchmarks for models s1.1-7B and Qwen3-8B, sometimes surpassing the full-weight models. The authors also examine how different layer-wise sparsity allocation strategies affect these outcomes.
By Ocean Monjur, Shahriar Kabir Nahin, Anshuman Chhabra
arXiv:2610.19121v1 Announce Type: new
Abstract: Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reason...
By Woosang Lim, Haneul Yoo, Vincent-Daniel Yun, Sungjoo Yoo
arXiv:2510.01581v2 Announce Type: replace-cross
Abstract: Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in l...
By Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad, Elias Stengel-Eskin, Akshay Nambi, Mohit Bansal
The paper introduces a pruning method for test-time reasoning that directly optimizes pass@$k$ by selecting a pool of partial solutions that maximizes the chance of at least one correct final answer. This approach balances quality and diversity automatically, avoiding separate design choices for each. Experiments on eight open-weight reasoning models show that the method outperforms existing quality-based and diversity-based pruning strategies, especially when a moderate number of partial solutions are retained.
By Mahdi Salmani, Adel Javanmard, Devansh Gupta, Meisam Razaviyayn
arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.
By Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi
arXiv:2508. 02178v3 Announce Type: replace Abstract: Large reasoning models (LRMs) often exhibit overthinking, producing verbose Chain-of-Thought (CoT) traces that increase inference cost and obscure the underlying reasoning process.
By Taihang Zhen, Jialiang Hong, Kai Chen, Guang Yang, Junlan Feng, Wenpeng Zhu, Jing Huo, Yang Gao, Depeng Wang, Haitao Wan, Xi Yang, Fanyu Meng, Yuyao Zhang, Ji Qi, Xiangyu Zhou
Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, these models often exhibit "computational overthinking," generating redundant reasoning steps that increase latency and cost without improving accuracy.
arXiv:2607. 19962v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) often suffer from overthinking due to redundant verification steps.
By Xinbang Dai, Zheyu Xin, Huikang Hu, Lin Ren, Rihui Jin, Guohui Xiao, Guilin Qi, Kuicai Dong, Zhaocheng Du, Yuyang Zhang
arXiv:2610.18862v1 Announce Type: cross
Abstract: Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existin...
By Minghao Luo, Liang Chen
The paper studies how to compress Chain-of-Thought (CoT) reasoning traces for smaller models. It examines three compression dimensions—importance criterion, restructuring level, and compression budget—across Math and General domains and Long/Short CoT regimes. Findings show that step-level pruning works best for shared reasoning backbones, token-level pruning needs symbol-aware signals, domain-specific restructuring effects differ, and training-time compression may not reduce inference cost, especially for Long-CoT students.
By Siyang Lyu, Xinghao Chen, Zhijing Sun, Tong Liu, Dawei Zhu, Xiaoyu Shen
The paper evaluates entropy-based pruning for compressing Chain-of-Thought (CoT) reasoning in large models. Across multiple models and tasks, low- and high-entropy step selection shows no advantage over random pruning, and low-entropy token retention only helps on mathematical benchmarks due to the low entropy of numeric tokens. Patching a few CoT tokens with their original activations restores near-perfect performance, indicating that task information is distributed throughout the entire reasoning chain rather than concentrated in a small set of tokens.
By Sara Candussio, Daniel Scalena, Luca Bortolussi, Elisabetta Fersini, Malvina Nissim, Gabriele Sarti