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The paper investigates whether a sparse dictionary can reuse the routing decisions already made by a mixture‑of‑experts (MoE) model. Using the NR‑SAE approach, each routed expert is given a small dictionary that is opened only when the frozen router selects that expert. Experiments on Gemma 4 26B‑A4B show that the expert‑aligned autoencoder explains 51.8 % of the variance in router‑weighted expert outputs, outperforming several control variants and suggesting that the router already encodes useful information that a dictionary would otherwise need to learn.
arXiv:2610.28369v1 Announce Type: cross Abstract: A growing body of work proposes dynamic expert-skipping methods for Mixture-of-Experts (MoE) models that adjust the number of experts activated per t...
IntBMoE introduces a block‑conditioned mixture‑of‑experts that decouples participation, execution, and materialization by combining dense expert composition with sparse block execution. Each internal layer uses a lightweight hypernetwork to merge all expert bases into a single composed expert, while a router selects only a few blocks per token, keeping compute and memory costs low. Experiments on image classification, language modeling, and sequential recommendation demonstrate consistent performance gains, and the model is deployed in AMap’s generative recommendation system, improving UVCTR by 2.4% in online A/B tests.
The paper investigates how to make Mixture-of-Experts (MoE) reward models more interpretable and useful for personalization by focusing on sparsity and diversity regularizers. By keeping the architecture and training data constant, the authors show that conventional MoE reward models learn entangled components that hinder both interpretability and personalization. Introducing a sparse MoE objective reduces entanglement, yielding experts that align with semantic categories and functional strengths, and enables composable, few‑shot personalization with a 25.81‑point improvement over entangled models.
arXiv:2609.36222v1 Announce Type: new Abstract: Large language models are increasingly expensive to serve. In large-scale serving systems, autoregressive decoding is often bottlenecked by transferrin...
arXiv:2608. 07814v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models deliver high capacity at low per-token compute, but deploying them cheaply requires compressing their many expert weight matrices.