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arXiv · 2609.37533

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

Arseny Ivanov·Alexander Kolesov·Alexander Korotin·Ivan Oseledets·Mikhail Goncharov
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Abstract

Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically factorized over positions, limiting sample quality in the few-step regime where diffusion's speed advantage over autoregressive decoding matters most. A recent line of work introduces a continuous Gaussian latent, trained as a variational autoencoder, to capture correlations across positions, but such approaches are prone to posterior collapse, where the latent is silently ignored. We propose Enhanced Mixture-of-Experts (E-MoE), which builds the reverse process as a mixture of factorized distributions over a discrete shared latent given by the expert-routing decisions of a Mixture-of-Experts (MoE) backbone, without increasing active parameters over the factorized baseline. Across synthetic multi-modal benchmarks, binarized MNIST, and LM1B, E-MoE improves few-step generation over factorized baselines.

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Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov. 2026-09-29. E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models. https://arxiv.org/abs/2609.37533

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