AI BriefingToMoE Converts Dense LLMs into MoE
Key point
The ToMoE technique has been released, converting dense LLMs into MoE via dynamic structural pruning to reduce active parameters without performance degradation.
Details
To address the high computational and memory costs of large language models (LLMs), the ToMoE technique was proposed. While existing structural pruning methods permanently remove unnecessary parameters, causing performance degradation, ToMoE applies dynamic structural pruning to maintain a fixed number of active parameters without deleting them.
This method transforms MLP layers into a Mixture of Experts (MoE) architecture and consistently outperforms existing structural pruning techniques even without fine-tuning. It has been validated across various model families, including Phi-2, LLaMA-2, LLaMA-3, and Qwen-2.5, and the related paper was accepted to ICML 2026.
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