# OBC-Prune: Outcome-Based Calibration for Large Reasoning Model Pruning

> 标题为英语

> 全文为英语

**Published:** 2026年9月17日
**Author:** Ha Lan Nguyen, Huy Hoang Tran, Trac-Duy Tran, Dung D. Le
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/obc-prune-outcome-based-calibration-for-large-reasoning-model-pruning

Large reasoning models (LRMs) generate long chain-of-thought traces before answering, creating significant inference overhead. Pruning can reduce this cost, but its effectiveness depends on the calibration data used to estimate parameter importance. Recent work calibrates on the…

> 内容来自原始来源： **arXiv — cs.AI**
> [在 arXiv — cs.AI 阅读全文](https://arxiv.org/abs/2609.17890)

**标签：** #arXiv

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