Title: From Score Distributions to Balance:  Plug-and-Play Mixture-of-Experts Routing
Primary area: foundation or frontier models, including LLMs
Keywords: Mixture-of-Experts, Load Balancing, Inference Optimization, Expert Routing, Large Language Models

Abstract: Mixture-of-Experts (MoE) models can scale parameter capacity by routing each
token to a subset of experts through a learned gate function. While conditional
routing reduces training costs, it shifts the burden on inference memory: expert
parameters and activations consume memory, limiting the number of experts per
device. As tokens are routed, some experts become overloaded while others are
underutilized. Because experts are mapped to GPUs, this imbalance translates di-
rectly into degraded system performance in terms of latency, throughput, and cost.
We present LASER, a plug-and-play, inference-time routing algorithm that bal-
ances load while preserving accuracy. LASER adapts to the shape of the gate’s
score distribution. When scores provide a clear preference, it routes to the
strongest experts; when scores are more uniform, it broadens the set of viable ex-
perts and routes to the least-loaded among them. Because LASER relies only on
gate scores from a trained model, it integrates directly into existing MoE inference
pipelines without retraining or finetuning. We evaluate LASER on Mixtral-8×7B
and DeepSeek-MoE-16b-chat across four datasets (ARC-Easy, ARC-Challenge,
MMLU, and GSM8K). LASER improves load balancing, translating into lower
latency and higher throughput, while keeping accuracy changes negligible

TL;DR: LASER adapts to gate score distributions at inference, reducing MoE imbalance without sacrificing accuracy; best of all, it drops in plug-and-play, no retraining needed.