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Expert Divergence Learning for MoE-based Language Models
arXiv – CS AI|Jiaang Li, Haibin Chen, Langming Liu, Yujin Yuan, Yadao Wang, Yizhen Zhang, Chengting Yu, Xin Tong, Weidong Zhang, Shilei Liu, Wenbo Su, Bo Zheng||6 views
🤖AI Summary
Researchers introduce Expert Divergence Learning, a new pre-training strategy for Mixture-of-Experts language models that prevents expert homogenization by encouraging functional specialization. The method uses domain labels to maximize routing distribution differences between data domains, achieving better performance on 15 billion parameter models with minimal computational overhead.
Key Takeaways
- →Expert Divergence Learning addresses the critical problem of expert homogenization in MoE language models where experts learn redundant functionalities.
- →The method uses Jensen-Shannon Divergence optimization to create specialized routing policies for different data domains during pre-training.
- →Models up to 15 billion parameters showed improved language modeling loss and downstream benchmark performance when trained with this approach.
- →The technique achieves expert specialization with negligible additional computational overhead during training.
- →Experimental validation confirms the method effectively mitigates expert redundancy and promotes functional specialization.
#mixture-of-experts#language-models#pre-training#model-architecture#expert-specialization#scaling#llm#deep-learning#optimization
Read Original →via arXiv – CS AI
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