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About this paper
Zhongyang Li, Ziyue Li, Tianyi Zhou
Routing Manifold Alignment (RoMA) enhances generalization in Mixture-of-Experts (MoE) Large Language Models by aligning routing weight manifolds with task embedding manifolds. This method achieves 7-15% accuracy improvements across eight benchmarks while operating efficiently through lightweight router fine-tuning without increasing inference costs.
Key insights
- The suboptimality of MoE LLM routers is attributed to a "manifold misalignment" between task embedding spaces and routing weight spaces.
- Explicitly aligning these manifolds ensures semantically similar tasks are consistently routed to similar experts, improving generalization and coherent knowledge utilization.
- Substantial performance gains can be achieved efficiently by regularizing and fine-tuning only the router components post-training, without incurring additional inference latency.
Summary by alphaXiv.
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