arXiv：cs.CL· Minh-Anh Nguyen, Nguyen T. Ha Lan, Xiyan Fu, Dung D. Le, Wei Lu·· 10 小时前
大推理模型剪枝后为何停不下来：TGAS 免训练恢复方法
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models
AI 导读
研究发现剪枝大推理模型（LRM）的精度下降多源于无法停止而非推理能力受损：稀疏度提高后模型常在正确答案已出现后仍陷入重复循环，超出 token 预算，但它们仍能识别密集模型的停止位置。
正文
Abstract:Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much of this degradation stems from a failure to stop rather than to reason. As sparsity increases, pruned LRMs increasingly fail to terminate within the token budget, often after the correct answer has appeared in their reasoning, instead falling into repetitive loops, even though they still recognize where the dense model would stop. We further find that the hidden-state difference between terminating and looping continuations is consistent across pruned checkpoints, suggesting a shared direction associated with committing to an answer. Building on this, we propose Termination-Guided Activation Steering (TGAS), a training-free method that adds this direction to the residual stream at inference time. Across six LRMs, three pruning pipelines, and math and code benchmarks, TGAS improves termination and overall accuracy while reducing repetitive reasoning and generation length, showing that this failure is actionable.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19934 [cs.CL] |
| (or arXiv:2610.19934v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19934 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Minh Nguyen [view email]
[v1]
Wed, 14 Oct 2026 10:19:15 UTC (738 KB)
来源：arXiv：cs.CL · arxiv.org