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Does a Global Perspective Help Prune Sparse MoEs Elegantly?

arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoEs) offer a promising alternative by activating only a subset of experts per forward pass, improving efficiency without sacrificing performance. However, the large number of expert parameters still leads to substantial memory consumption. Existing pruning methods typically allocate budgets uniformly across layers, overlooking the heterogeneous redundancy that arises in sparse MoEs. We propose GRAPE (Global Redundancy-Aware Pruning of Experts, a global pruning strategy that dynamically allocates pruning budgets based on cross-layer redundancy. Experiments on Mixtral-8x7B, Mixtral-8x22B, DeepSeek-MoE, Qwen-MoE, and GPT-OSS show that, under the same pruning budget, GRAPE consistently achieves the best average performance. On the three main models reported in the

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Zeliang Zhang, Nikhil Ghosh, Jiani Liu, Bin Yu, Xiaodong Liu · · 1 min read · 5 views
#cs.CL

arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoEs) offer a promising alternative by activating only a subset of experts per forward pass, improving efficiency without sacrificing performance. However, the large number of expert parameters still leads to substantial memory consumption. Existing pruning methods typically allocate budgets uniformly across layers, overlooking the heterogeneous redundancy that arises in sparse MoEs. We propose GRAPE (Global Redundancy-Aware Pruning of Experts, a global pruning strategy that dynamically allocates pruning budgets based on cross-layer redundancy. Experiments on Mixtral-8x7B, Mixtral-8x22B, DeepSeek-MoE, Qwen-MoE, and GPT-OSS show that, under the same pruning budget, GRAPE consistently achieves the best average performance. On the three main models reported in the paper, it improves average accuracy over the strongest local baseline by 1.40% on average across pruning settings, with gains of up to 2.45%.

Executive Summary

The paper "Does a Global Perspective Help Prune Sparse MoEs Elegantly?" introduces GRAPE, a novel global pruning strategy for Sparse Mixture-of-Experts (MoEs) models. Addressing the critical challenge of high memory consumption in large LLMs despite their computational efficiency, GRAPE dynamically allocates pruning budgets across layers, recognizing and exploiting heterogeneous redundancy. Unlike conventional uniform budget allocation, this global approach significantly improves performance under equivalent pruning budgets. Empirical evaluations across various MoE architectures, including Mixtral-8x7B and DeepSeek-MoE, demonstrate GRAPE's superior accuracy, consistently outperforming strong local baselines and offering substantial gains, thereby promising more memory-efficient and performant large language models.

Key Points

  • ▸ Sparse MoEs offer computational efficiency but suffer from high memory consumption due to numerous expert parameters.
  • ▸ Existing pruning methods often fail to account for heterogeneous redundancy across different layers in MoEs.
  • ▸ GRAPE (Global Redundancy-Aware Pruning of Experts) is a proposed global pruning strategy that dynamically allocates pruning budgets based on cross-layer redundancy.
  • ▸ GRAPE consistently achieves superior performance compared to local pruning baselines under the same pruning budget across diverse MoE models.
  • ▸ The method demonstrates average accuracy improvements of 1.40% and up to 2.45% over the strongest local baseline on key models.

Merits

Novelty in Pruning Strategy

GRAPE's shift from uniform to dynamic, global budget allocation for pruning is a significant conceptual advance, directly addressing a recognized weakness in prior methods by leveraging cross-layer redundancy.

Empirical Rigor and Breadth

The evaluation across multiple prominent MoE architectures (Mixtral, DeepSeek-MoE, Qwen-MoE, GPT-OSS) lends strong credibility to the method's generalizability and robustness, demonstrating consistent performance gains.

Directly Addresses a Critical Problem

The paper tackles the escalating memory costs of LLMs, particularly MoEs, which is a major bottleneck for wider deployment and research, offering a practical solution without sacrificing performance.

Demerits

Limited Theoretical Justification

While empirically effective, the paper could benefit from deeper theoretical exploration of why cross-layer redundancy manifests and why GRAPE's dynamic allocation optimally exploits it, beyond just empirical observation.

Computational Overhead of Global Pruning

The paper does not explicitly detail the computational cost or complexity associated with determining and dynamically allocating global pruning budgets, which could be non-trivial for very large models.

Specificity of 'Global Perspective'

The term 'global' is used, but the scope of 'global' within a model (e.g., across all layers, or within specific blocks) could be more precisely defined, and its scalability to extremely deep networks discussed.

Expert Commentary

This paper presents a compelling argument for moving beyond layer-wise heuristics in MoE pruning, a domain ripe for innovation. The core insight—that redundancy is not uniformly distributed across layers—is intuitively sound yet often overlooked in practice. GRAPE's empirical success across a diverse suite of MoE models is particularly convincing, signaling a robust and generalizable approach. However, a deeper theoretical exposition on the underlying mechanisms driving this heterogeneous redundancy would significantly strengthen the paper's academic contribution. For instance, is this a function of particular architectural choices, or a more fundamental property of how knowledge is distributed and processed in deep networks? Furthermore, while the performance gains are clear, a more thorough analysis of the computational overhead involved in the global budget allocation process itself is warranted, especially for practitioners considering implementation on massive models. The work is a vital step towards more resource-efficient LLMs, crucial for both sustainability and broader accessibility.

Recommendations

  • ✓ Conduct a deeper theoretical analysis to explain the observed heterogeneous redundancy and GRAPE's effectiveness, potentially linking it to information theory or network dynamics.
  • ✓ Provide a detailed analysis of the computational complexity and runtime overhead associated with GRAPE's global pruning budget determination, especially for very large models.
  • ✓ Investigate the interplay between GRAPE and other optimization techniques (e.g., quantization, distillation) to explore potential synergistic effects for even greater efficiency gains.

Sources

Original: arXiv - cs.CL

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