评分 6 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-17
评分依据：Pruning redundant CoT reasoning via small-scale preference optimization, directly addresses reasoning efficiency
arXiv:2508.10164v2 Announce Type: replace Abstract: Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs increase computational costs and may lead to overthinking, raising challenges in balancing reasoning effectiveness and efficiency. Current solutions often compromise reasoning quality or require extensive resources. In this paper, we investigate how to reduce the generation length of LRMs with limited tuning.