基于重量的自适应专家负载平衡方案,用于能源部的动态路由
Jialin Wen1, Xiaojun Li1, Junping Yao1
1School of Computer Science, Rocket Force University of Engineering, Xi'an, Shaanxi, China.
Frontiers in neurorobotics
|October 30, 2025
概括
本研究介绍了一种动态路由策略,用于解决混合专家 (MoE) 模型中的负载不平衡. 新的方法有效地平衡专家负载,没有辅助损失,提高自然语言理解任务的效率和性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 负载失衡是培训专家混合 (MoE) 模型中的一个关键性能问题,可能导致路由崩.
- 当前的解决方案通常依赖于具有任务特定超参数的辅助损失函数,从而限制了它们的概括性.
- 增加专家激活可能会加剧不平衡,而固定激活可能会损害模型在复杂任务上的性能.
研究的目的:
- 为MoE模型提出一种新的动态平衡的路由策略.
- 为了解决现有的负载均衡方法的局限性,例如超参数调整和梯度扰动.
- 为了提高计算资源利用率和模型性能.
主要方法:
- 引入了一个基于值的动态路由算法,以便在每个路由步骤后调整专家权重.
- 该策略直接操纵路由以影响负载分配,绕过了辅助损失函数的需要.
- 基于值的动态专家激活用于优化参数使用.
主要成果:
- 拟议的方法的准确性可与自然语言理解 (NLU) 基准的前2路由相提并论.
- 观察到负载标准偏差显著减少 (例如,在MNLI上从12.25到1.18)
- 这种方法有效地减轻负载不平衡,而不会引入梯度扰动.
结论:
- 动态平衡的路由策略为MoE负载平衡提供了基于损失函数的有效替代方法.
- 这种方法提高了计算效率,并保持了高模型准确度.
- 基于值的动态专家激活为优化MoE架构提供了一个新的途径.
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