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