MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE

• URL: http://arxiv.org/abs/2509.17238v2
• Date: Mon, 13 Oct 2025 16:48:37 GMT
• Title: MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
• Authors: Soheil Zibakhsh, Mohammad Samragh, Kumari Nishu, Lauren Hannah, Arnav Kundu, Minsik Cho,
• Abstract summary: We introduce hyper-parallel scaling, a complementary framework that improves prediction quality at the token level.<n>We implement this concept in Mixture-of-Experts (MoE) models, which we refer to as Roster of Experts (RoE)<n>RoE is a training-free inference algorithm that turns a single MoE into a dynamic ensemble of MoEs.
• Score: 12.96406947372715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generation quality of large language models (LLMs) is often improved by utilizing inference-time sequence-level scaling methods (e.g., Chain-of-Thought). We introduce hyper-parallel scaling, a complementary framework that improves prediction quality at the token level. Hyper-parallel scaling computes and aggregates multiple output proposals for a single token from the model. We implement this concept in Mixture-of-Experts (MoE) models, which we refer to as Roster of Experts (RoE). RoE is a training-free inference algorithm that turns a single MoE into a dynamic ensemble of MoEs. RoE injects controlled stochasticity into the expert routing mechanism, enabling it to sample multiple diverse experts for each token and aggregate their outputs for a more accurate final prediction. To overcome the computational cost, we introduce an efficient batching strategy and a specialized KV-caching mechanism that minimizes compute and memory overhead. For example, RoE enables a 7B MoE model to match the performance of a 10.5B MoE model while using 30% less compute for inference. These gains are achieved without any fine-tuning of model parameters.

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