---
title: "Who cites GRIN: GRadient-INformed MoE — 7 citing papers"
description: "7 papers of the Elamur Research library cite «GRIN: GRadient-INformed MoE», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [GRIN: GRadient-INformed MoE](https://md.research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/) / cited by

# Who cites «GRIN: GRadient-INformed MoE»

7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «GRIN: GRadient-INformed MoE»](https://md.research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/)

1. **[Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models](https://md.research.elamur.ai/free/en/papers/qiu-2025-demons-detail-implementing-load-balancing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihan Qiu, Zeyu Huang, Bo Zheng, et al.*2025 · 21 citations*
2. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
3. [**Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale arXiv:2603.25040**Yicheng Zou, Dongsheng Zhu, Lin Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/zou-2026-intern-s1-pro-scientific/)

4 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/) — GRIN: GRadient-INformed MoE