@inproceedings{zhou-etal-2025-quzo,
title = "{Q}u{ZO}: Quantized Zeroth-Order Fine-Tuning for Large Language Models",
author = "Zhou, Jiajun and
Yang, Yifan and
Zhen, Kai and
Liu, Ziyue and
Zhao, Yequan and
Banijamali, Ershad and
Mouchtaris, Athanasios and
Wong, Ngai and
Zhang, Zheng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.271/",
pages = "5341--5359",
ISBN = "979-8-89176-332-6",
abstract = "Large Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various downstream tasks. Traditional fine-tuning methods such as stochastic gradient descent and Adam optimization require backpropagation, which is error-prone in the low-precision settings. To overcome these limitations, we propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision (e.g., 4- or 8-bit) forward passes. Our method avoids the low-precision straight-through estimator, which requires backward computation, and instead utilizes optimized stochastic rounding to mitigate increased bias. QuZO simplifies the training process, while achieving results comparable to first-order methods in ${\rm FP}8$ and superior accuracy in ${\rm INT}8$ and ${\rm INT}4$ training. Experiments demonstrate that QuZO achieves competitive performance on classification, multi-choice, and generation tasks under low-bit training, including zero-shot reasoning tasks. Notably, QuZO incurs minimal overhead and reduces memory consumption by $2.94 \times${--}$5.47 \times$ compared to quantized first-order methods during LLaMA-7B fine-tuning."
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<abstract>Large Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various downstream tasks. Traditional fine-tuning methods such as stochastic gradient descent and Adam optimization require backpropagation, which is error-prone in the low-precision settings. To overcome these limitations, we propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision (e.g., 4- or 8-bit) forward passes. Our method avoids the low-precision straight-through estimator, which requires backward computation, and instead utilizes optimized stochastic rounding to mitigate increased bias. QuZO simplifies the training process, while achieving results comparable to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. Experiments demonstrate that QuZO achieves competitive performance on classification, multi-choice, and generation tasks under low-bit training, including zero-shot reasoning tasks. Notably, QuZO incurs minimal overhead and reduces memory consumption by 2.94 \times–5.47 \times compared to quantized first-order methods during LLaMA-7B fine-tuning.</abstract>
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%0 Conference Proceedings
%T QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
%A Zhou, Jiajun
%A Yang, Yifan
%A Zhen, Kai
%A Liu, Ziyue
%A Zhao, Yequan
%A Banijamali, Ershad
%A Mouchtaris, Athanasios
%A Wong, Ngai
%A Zhang, Zheng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhou-etal-2025-quzo
%X Large Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various downstream tasks. Traditional fine-tuning methods such as stochastic gradient descent and Adam optimization require backpropagation, which is error-prone in the low-precision settings. To overcome these limitations, we propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision (e.g., 4- or 8-bit) forward passes. Our method avoids the low-precision straight-through estimator, which requires backward computation, and instead utilizes optimized stochastic rounding to mitigate increased bias. QuZO simplifies the training process, while achieving results comparable to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. Experiments demonstrate that QuZO achieves competitive performance on classification, multi-choice, and generation tasks under low-bit training, including zero-shot reasoning tasks. Notably, QuZO incurs minimal overhead and reduces memory consumption by 2.94 \times–5.47 \times compared to quantized first-order methods during LLaMA-7B fine-tuning.
%U https://aclanthology.org/2025.emnlp-main.271/
%P 5341-5359
Markdown (Informal)
[QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models](https://aclanthology.org/2025.emnlp-main.271/) (Zhou et al., EMNLP 2025)
ACL
- Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, and Zheng Zhang. 2025. QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 5341–5359, Suzhou, China. Association for Computational Linguistics.