Beijing, China – DeepSeek-R1, known for its use of the Mixture-of-Experts (MoE) technique, may be on the verge of a significant performance boost. Researchers are constantly seeking ways to optimize MoE models, and a recent breakthrough by Zihan Wang, a Ph.D. student in Computer Science at Northwestern University, promises a substantial leap forward. Wang and his team have developed a novel approach called Chain of Experts (CoE), which, according to their experiments, outperforms existing MoE models across several key metrics.
The core innovation of CoE lies in its enhanced information processing capabilities within Large Language Models (LLMs). While the specifics of the CoE architecture are detailed in Wang’s publicly available blog posts (linked below), the preliminary findings suggest a more efficient and effective utilization of experts within the MoE framework.
The current MoE technology still has significant room for optimization, Wang notes in his blog. His research aims to unlock the communication power of MoEs, leading to improved performance, scalability, resource efficiency, and expert utilization.
The potential impact of CoE on models like DeepSeek-R1 is considerable. By optimizing the MoE architecture, CoE promises a free lunch in terms of performance gains, without requiring additional computational resources or retraining from scratch. This is particularly significant in the rapidly evolving field of LLMs, where efficiency and scalability are paramount.
Wang has already released the code for CoE on GitHub, allowing other researchers and developers to experiment with and build upon his work. The release of the research paper is expected soon, providing a more in-depth technical explanation of the CoE architecture and its advantages.
The research is particularly timely given the increasing adoption of MoE models in state-of-the-art LLMs. As these models continue to grow in size and complexity, efficient expert utilization becomes crucial for maintaining performance and managing computational costs. CoE offers a promising pathway towards achieving these goals.
Links:
- Code: https://github.com/ZihanWang314/coe
- Chinese Report: https://sandy-server-87f.notion.site/1ab9bb750b79801bbfebf01ae9a77b3f
- English Report: https://sandy-server-87f.notion.site/Chain-of-Experts-Unlocking-the-Communication-Power-of-MoEs-1ab9bb750b7980048d43e
Conclusion:
The development of Chain of Experts (CoE) represents a significant step forward in optimizing Mixture-of-Experts (MoE) models for Large Language Models (LLMs). By improving performance, scalability, and resource efficiency, CoE has the potential to unlock new capabilities for LLMs and accelerate their adoption across various applications. The open-source release of the CoE code encourages further research and development in this promising area, paving the way for even more efficient and powerful LLMs in the future. The upcoming research paper will undoubtedly provide a more comprehensive understanding of the CoE architecture and its potential impact on the field.
References:
- Wang, Z. (2024). Chain of Experts: Unlocking the Communication Power of MoEs. [Blog Post]. Retrieved from https://sandy-server-87f.notion.site/Chain-of-Experts-Unlocking-the-Communication-Power-of-MoEs-1ab9bb750b7980048d43e
- Wang, Z. (2024). Chain of Experts: Unlocking the Communication Power of MoEs. [GitHub Repository]. Retrieved from https://github.com/ZihanWang314/coe
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