---
title: "Seoul National University's Cluster-aware Upcycling Turns Cloned AI Experts Into Real Specialists"
subtitle: "Researchers from Seoul National University and LG AI Research propose a smarter way to initialize Mixture-of-Experts models so experts specialize from step one."
url: "https://alphasignal.ai/news/seoul-national-university-s-cluster-aware-upcycling-turns-cloned-ai-experts"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-04-15T00:00:00.000Z"
updated: "2026-08-23T22:02:55.475Z"
type: "Paper"
topics:
  - "Llms"
  - "Post Training"
subtopics:
  - "Mixture Of Experts"
  - "Distillation"
  - "Fine Tuning"
upvotes: 850
source_url: "https://arxiv.org/abs/2604.13508"
comments: 0
truncated: true
---

# Seoul National University's Cluster-aware Upcycling Turns Cloned AI Experts Into Real Specialists

> Researchers from Seoul National University and LG AI Research propose a smarter way to initialize Mixture-of-Experts models so experts specialize from step one.

Turning a pretrained dense transformer into a Mixture-of-Experts (MoE) model is one of the cheapest ways to scale capacity without paying the full pretraining bill. The standard trick, called Sparse Upcycling, duplicates the dense feed-forward network into N identical experts and adds a random router on top. It works, but a nagging problem remains: every expert starts as a clone, so they take a long time to actually differentiate.

A team from Seoul National University and LG AI Research has a fix. In a paper accepted to CVPR 2026, Sanghyeok Chu, Pyunghwan Ahn, Gwangmo Song, Seung Hwan Kim, Honglak Lee, and Bohyung Han introduce [Cluster-aware Upcycling](https://sanghyeokchu.github.io/cluster-aware-upcycling/), an initialization scheme that bakes semantic structure into the experts and the router before training begins. [The paper](https://arxiv.org/abs/2604.13508) shows the approach beats existing upcycling methods on CLIP ViT-B/32 and ViT-B/16 across zero-shot and few-shot benchmarks.

## Why cloned experts are a lousy starting point

Sparse Upcycling initializes an MoE from pretrained dense weights instead of training from scratch. Because all experts start from identical weights and the router is randomly initialized, the model suffers from expert symmetry and limited early specialization. In practice the router has no reason to send a given token to any particular expert, and the experts have no reason to diverge. Training eventually breaks the tie, but a lot of compute burns before the model becomes a real mixture of specialists.

![Comparison between Sparse Upcycling with random routing and Cluster-aware Upcycling with specialized experts](https://arxiv.org/html/2604.13508v2/concept.png)

## Cluster first, then carve up the weight matrix

The core idea is to give each expert a different slice of the input distribution from the start. The method first partitions the dense model's input activations into semantic clusters, then initializes each expert using the subspace representations of its corresponding cluster via truncated SVD, while setting the router's initial weights to the cluster centroids.

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## Sources

- [Enhancing Mixture-of-Experts Specialization via Cluster-Aware Upcycling (arXiv)](https://arxiv.org/abs/2604.13508)
- [Cluster-Aware Upcycling Project Page](https://sanghyeokchu.github.io/cluster-aware-upcycling/)
- [CVPR 2026 Open Access - Cluster-Aware Upcycling](https://openaccess.thecvf.com/content/CVPR2026/papers/Chu_Enhancing_Mixture-of-Experts_Specialization_via_Cluster-Aware_Upcycling_CVPR_2026_paper.pdf)
- [Sanghyeok Chu - Personal Site](https://sanghyeokchu.github.io/)