---
title: "Who cites Chao Du — 352 papers of our library"
description: "352 papers of the Elamur Research library cite the works of Chao Du, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/chao-du/cited-by/"
---
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# Who cites Chao Du

353 citations of their works from 352 papers of our library (of the 141,840 whose bibliographies we read) · 11 with a page here, the most cited first · [← Chao Du](https://md.research.elamur.ai/free/en/authors/chao-du/)

1. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
2. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paper**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
3. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 174 citations*
4. [**SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild arXiv:2503.18892**Weihao Zeng, Yuzhen Huang, Qian Liu, et al.*2025 · 148 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2025-simplerl-zoo-investigating-taming-zero/)
5. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
6. **[Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model](https://md.research.elamur.ai/free/en/papers/hu-2025-open-reasoner-zero-open-source/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jingcheng Hu, Yinmin Zhang, Qi Han, et al.*2025 · 121 citations*
7. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
8. [**MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention arXiv:2506.13585**MiniMax, Aili Chen, Aonian Li, et al.*2025 · 58 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/)
9. [**StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements**Jillian Fisher, Skyler Hallinan, Ximing Lu, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/fisher-2024-styleremix-interpretable-authorship-obfuscation-distillation/)
10. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
11. **[Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety](https://md.research.elamur.ai/free/en/papers/natale-2026-beyond-simple-term-injection-reasoning/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Paolo Di Natale, Elena Chiocchetti, Marlies Alber, et al.*2026*

341 more citing papers of our library have no page here yet.

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