Accelerating Dense LLMs via L0-regularized Mixture-of-Experts
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Computer Science > Artificial Intelligence
Title:Accelerating Dense LLMs via L0-regularized Mixture-of-Experts
Abstract:Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degradation, while Mixture-of-Experts (MoE) models require extensive computational resources. In this paper, we propose L0-MoE, a lightweight MoE approach using L0-regularization to accelerate dense LLMs nearly without performance loss. Our method introduces a cluster confusion matrix for domain-aware dataset curation and applies dynamic batching for efficient training. Experiments show that L0-MoE achieves up to 2.5x speedup over dense models while maintaining competitive performance, outperforming existing LLM acceleration baselines.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.21672 [cs.AI] |
| (or arXiv:2609.21672v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.21672
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025 |
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