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SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs

Yifan Yang, Hanling Yi, Dongquan Yang, Feng Lin, Rong Xiao, Aijun Li, Xiaotian Yu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29662 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Mixer-of-Expert (MoE) arsitektur mengukur kapasitas model secara efisien, namun jejak memori mereka yang signifikan merupakan hambatan utama untuk pengembangan. Mixer-of-Expert mengurangi jejak ini, tetapi kebanyakan metode berfokus pada mana para ahli untuk menghapus dan memotong setiap lapisan secara merata. Kami mengusulkan SPARE (Sparsity Allocation via Routing Entropy), metode yang bebas pelatihan, selector-agnostic yang memutuskan berapa banyak ahli untuk menghapus dari setiap lapisan menggunakan entropy routing, entropy token 'normalised Top-K gate weighs pada data kalibrasi. Ketika berat gerbang token disebarkan secara merata, menghapus satu ahli aktif membuang sedikit dari itu, jadi SPARE memotong lapisan dengan lebih banyak routing lebih agresif. gangguan terikat di bawah

Machine translation (Meta M2M100); the English original follows.

Abstract

Mixture-of-Experts (MoE) architectures scale model capacity efficiently, yet their substantial memory footprint poses a major barrier to deployment. Expert pruning reduces this footprint, but most methods focus on which experts to remove and prune every layer equally. We propose SPARE (Sparsity Allocation via Routing Entropy), a training-free, selector-agnostic method that decides how many experts to remove from each layer using routing entropy, the entropy of tokens' normalized Top-K gate weights on calibration data. When a token's gate weight is spread evenly, removing one active expert discards little of it, so SPARE prunes layers with more diffuse routing more aggressively. A perturbation bound under random pruning formalizes this intuition, and controlled interventions with a fixed selector link concentrated routing to larger output changes. With the same selector and budget, SPARE improves average accuracy over uniform allocation on Mixtral, Qwen3, and DeepSeek models, especially at high pruning ratios. At 50% expert pruning, it raises the eight-task average of Qwen3-30B-A3B from 57.2% to 63.8%, and the math, code, and science average of DeepSeek-R1 from 80.1 to 82.8.

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Topics: Spare part, Computer science, Entropy (arrow of time), Mathematical optimization, Pruning, Routing (electronic design automation)

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