Structured Pruning for Diverse Best-of-N Reasoning Optimization

Hieu Trung Nguyen; Bao Nguyen; Viet Anh Nguyen

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arXiv · 2506.03978

Structured Pruning for Diverse Best-of-N Reasoning Optimization

Hieu Trung Nguyen·Bao Nguyen·Viet Anh Nguyen

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Abstract

Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work, we uncover a surprising phenomenon: the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune during inference. By aligning question embeddings with head embeddings, SPRINT identifies those pruned-head configurations that result in more accurate reasoning. Extensive experiments demonstrate that our method significantly outperforms traditional best-of-$N$ and random head selection strategies on the MATH500 and GSM8K datasets.

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BibTeX RIS

Hieu Trung Nguyen, Bao Nguyen, Viet Anh Nguyen. 2025-06-09. Structured Pruning for Diverse Best-of-N Reasoning Optimization. https://arxiv.org/abs/2506.03978

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