arXiv:2607.01710v1 Announce Type: new Abstract: Sparsely activated Mixture-of-Experts (MoE) language models contain substantial structured redundancy among routed experts, but pruning them without downstream calibration data remains challenging. Existing expert-pruning methods typically rely on a single aggregated importance score, which can bias the retained set toward experts favored by dominant calibration patterns. We propose \textbf{Generic TB-Coverage}, a coverage-aware expert pruning method that uses only generic text corpora (WikiText2 and C4) for calibration. Instead of collapsing exp
The proliferation of large language models (LLMs) and their computational demands has necessitated more efficient architectures, making pruning techniques a critical area of research right now.
This development proposes a method to optimize Sparse Mixture-of-Experts (MoE) language models more efficiently, reducing their computational overhead without extensive, specialized calibration, which could democratize access and deployment of powerful AI models.
Current expert-pruning methods for MoE models are often biased and resource-intensive for calibration; this new approach uses generic text corpora, simplifying and potentially improving the pruning process.
- · AI developers
- · Cloud providers
- · Researchers in efficient AI
- · Companies deploying LLMs
- · Developers of less efficient pruning methods
More efficient and compact MoE language models become available for deployment.
Reduced operational costs and computational requirements enable wider adoption and scale of advanced AI applications.
The democratization of powerful, specialized AI models could accelerate innovation in various sectors by lowering the barrier to entry for AI development.
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Read at arXiv cs.AI