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arXiv · 2603.13364

FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach

Ning Liao·Xiaoxing Wang·Xiaohan Qin·Junchi Yan
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Abstract

As revealed by the scaling law of fine-grained MoE, model performance ceases to be improved once the granularity of the intermediate dimension exceeds the optimal threshold, limiting further gains from single-dimension fine-grained design. To address this bottleneck, we propose FineRMoE (FineR-Grained MoE), an architecture that extends fine-grained expert design to both intermediate and output dimensions, aiming to enhance expert specialization beyond the single-dimension limit. We further introduce a bi-level sparse forward computation paradigm and a specialized routing mechanism to govern the activation. In addition, to obviate the prohibitive cost of training FineRMoE from scratch, we devise a generalized upcycling method to build FineRMoE in a cost-effective manner. Extensive experiments demonstrate the superior performance achieved by FineRMoE across ten standard benchmarks. Compared with the strongest baseline, FineRMoE achieves 6 times higher parameter efficiency, 281 times lower prefill latency, and 136 timese higher decoding throughput during inference.

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Ning Liao, Xiaoxing Wang, Xiaohan Qin, Junchi Yan. 2026-03-09. FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach. https://arxiv.org/abs/2603.13364

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