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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 model's own rollouts instead of generic dataset, but treats all reasoning tokens uniformly, regardless of whether they contribute to successful reasoning. As a result, pruning protects weights by statistical salience rather than by their contribution to correct reasoning, so weights behind erroneous computation survive
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T22:30:48.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.