Think Before You Prune: Selective Self-Generated Calibration for Pruning Large Reasoning Models
Published:Nov 24, 2025 08:08
•1 min read
•ArXiv
Analysis
This article likely discusses a novel method for pruning large language models (LLMs) to improve efficiency. The core idea seems to be a self-calibration technique that selectively identifies and addresses potential issues before pruning, aiming to maintain or improve the model's reasoning capabilities after the pruning process. The focus is on reasoning models, suggesting the method is tailored for tasks requiring complex logical deduction and problem-solving.
Key Takeaways
- •Focuses on pruning large reasoning models.
- •Employs a self-generated calibration technique.
- •Aims to maintain or improve reasoning capabilities after pruning.
- •Suggests a selective approach to calibration.
Reference
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