PrunePath: Towards Highly Structured Sparse Language Models

• URL: http://arxiv.org/abs/2605.28283v1
• Date: Wed, 27 May 2026 10:29:52 GMT
• Title: PrunePath: Towards Highly Structured Sparse Language Models
• Abstract summary: We introduce textbfPrunePath, a budget-adaptive structured sparsification framework for FFN layers.<n>PrunePath replaces independent expert-wise thresholding with a softmax-normalized routing distribution.<n>Across NLU, NLG, and instruction-tuning evaluations, PrunePath achieves a favorable sparsity--performance trade-off.
• Score: 8.390447915838122
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Feed-forward networks (FFNs) dominate the parameter count and computation of modern language models, yet existing pruning methods often struggle to convert sparsity into hardware-friendly inference efficiency gains. We introduce \textbf{PrunePath}, a budget-adaptive structured sparsification framework for FFN layers. Built on MoEfication, PrunePath replaces independent expert-wise thresholding with a softmax-normalized routing distribution and activates important experts under a cumulative-mass threshold. This formulation imposes a token-level probability budget, enabling adaptive expert counts and a direct inference-time sparsity knob from a single checkpoint. Across NLU, NLG, and instruction-tuning evaluations, PrunePath achieves a favorable sparsity--performance trade-off compared with existing static pruning and MoEfication-based methods. We further implement Triton kernels for KV-cache decoding to translate the resulting structured sparsity into practical memory savings and measurable decoding-speed improvements. These results demonstrate the superior performance of PrunePath for building highly sparse, deployment-friendly large language models.

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