LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing
arXiv:2603.12645v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) based Large Language Models (LLMs) have demonstrated impressive performance and computational efficiency. However, their deployment is often constrained by substantial memory demands, primarily due to the need to load numerous expert modules. While existing expert compression techniques like pruning or merging attempt to mitigate this, they often suffer from irreversible knowledge loss or high training overhead. In this paper, we propose a novel expert compression paradigm termed expert replacing, which replaces redundant experts with parameter-efficient modules and recovers their capabilities with low training costs. We find that even a straightforward baseline of this paradigm yields promising performance. Building on this foundation, we introduce LightMoE, a framework that enhances the paradigm by introducing adaptive expert selection, hierarchical expert construction, and an annealed recovery strategy. Experi
arXiv:2603.12645v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) based Large Language Models (LLMs) have demonstrated impressive performance and computational efficiency. However, their deployment is often constrained by substantial memory demands, primarily due to the need to load numerous expert modules. While existing expert compression techniques like pruning or merging attempt to mitigate this, they often suffer from irreversible knowledge loss or high training overhead. In this paper, we propose a novel expert compression paradigm termed expert replacing, which replaces redundant experts with parameter-efficient modules and recovers their capabilities with low training costs. We find that even a straightforward baseline of this paradigm yields promising performance. Building on this foundation, we introduce LightMoE, a framework that enhances the paradigm by introducing adaptive expert selection, hierarchical expert construction, and an annealed recovery strategy. Experimental results show that LightMoE matches the performance of LoRA fine-tuning at a 30% compression ratio. Even under a more aggressive 50% compression rate, it outperforms existing methods and achieves average performance improvements of 5.6% across five diverse tasks. These findings demonstrate that LightMoE strikes a superior balance among memory efficiency, training efficiency, and model performance.
Executive Summary
This article proposes a novel expert compression paradigm, expert replacing, to reduce the redundancy of Mixture-of-Experts (MoE) based Large Language Models (LLMs). By replacing redundant experts with parameter-efficient modules and recovering their capabilities with low training costs, the proposed framework, LightMoE, demonstrates promising performance while achieving a 30% compression ratio. Experimental results show that LightMoE outperforms existing methods at a 50% compression rate, achieving average performance improvements of 5.6% across five diverse tasks. This study highlights the potential of expert replacing to strike a balance among memory efficiency, training efficiency, and model performance.
Key Points
- ▸ Expert replacing paradigm reduces redundancy in MoE-based LLMs
- ▸ LightMoE framework enhances expert replacing with adaptive expert selection and hierarchical expert construction
- ▸ Experimental results demonstrate significant performance improvements at high compression ratios
Merits
Improved Memory Efficiency
The expert replacing paradigm allows for substantial memory savings by replacing redundant experts with parameter-efficient modules, making it an attractive solution for large-scale LLM deployments.
Enhanced Training Efficiency
The proposed framework enables low training costs for recovering expert capabilities, reducing the computational overhead associated with training MoE-based LLMs.
Superior Model Performance
Experimental results demonstrate that LightMoE achieves average performance improvements of 5.6% across five diverse tasks, outperforming existing methods at high compression ratios.
Demerits
Limited Generalizability
The study focuses on MoE-based LLMs and may not generalize to other types of neural networks or models.
Potential Knowledge Loss
The expert replacing paradigm may introduce some knowledge loss during the replacement process, which could impact model performance in certain applications.
Expert Commentary
This study represents a significant contribution to the field of neural network compression and Large Language Model deployment. The proposed expert replacing paradigm and the LightMoE framework demonstrate a promising approach to reducing redundancy in MoE-based LLMs while maintaining model performance. However, further research is needed to explore the generalizability of the proposed framework and to address potential knowledge loss associated with the replacement process.
Recommendations
- ✓ Future studies should investigate the application of the expert replacing paradigm to other types of neural networks and models.
- ✓ Researchers should explore the development of more efficient models for large-scale LLM deployments, considering the trade-offs between memory efficiency, training efficiency, and model performance.