@inproceedings{tang-etal-2025-middo,
title = "Middo: Model-Informed Dynamic Data Optimization for Enhanced {LLM} Fine-Tuning via Closed-Loop Learning",
author = "Tang, Zinan and
Gao, Xin and
Pei, Qizhi and
Pan, Zhuoshi and
Cai, Mengzhang and
Wu, Jiang and
He, Conghui and
Wu, Lijun",
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.350/",
pages = "6882--6902",
ISBN = "979-8-89176-332-6",
abstract = "Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce **Middo**, a self-evolving **M**odel-**i**nformed **d**ynamic **d**ata **o**ptimization framework that uses model-aware data selection and context-preserving data refinement. Unlike conventional one-off filtering/synthesis methods, our framework establishes a closed-loop optimization system: (1) A self-referential diagnostic module proactively identifies suboptimal samples through tri-axial model signals - *loss patterns (complexity)*, *embedding cluster dynamics (diversity)*, and *self-alignment scores (quality)*; (2) An adaptive optimization engine then transforms suboptimal samples into pedagogically valuable training points while preserving semantic integrity; (3) This optimization process continuously evolves with model capability through dynamic learning principles. Experiments on multiple benchmarks demonstrate that our consistently enhances the quality of seed data and boosts LLM{'}s performance with improving accuracy by 7.15{\%} on average while maintaining the original dataset scale. This work establishes a new paradigm for sustainable LLM training through dynamic human-AI co-evolution of data and models."
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<abstract>Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce **Middo**, a self-evolving **M**odel-**i**nformed **d**ynamic **d**ata **o**ptimization framework that uses model-aware data selection and context-preserving data refinement. Unlike conventional one-off filtering/synthesis methods, our framework establishes a closed-loop optimization system: (1) A self-referential diagnostic module proactively identifies suboptimal samples through tri-axial model signals - *loss patterns (complexity)*, *embedding cluster dynamics (diversity)*, and *self-alignment scores (quality)*; (2) An adaptive optimization engine then transforms suboptimal samples into pedagogically valuable training points while preserving semantic integrity; (3) This optimization process continuously evolves with model capability through dynamic learning principles. Experiments on multiple benchmarks demonstrate that our consistently enhances the quality of seed data and boosts LLM’s performance with improving accuracy by 7.15% on average while maintaining the original dataset scale. This work establishes a new paradigm for sustainable LLM training through dynamic human-AI co-evolution of data and models.</abstract>
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%0 Conference Proceedings
%T Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning
%A Tang, Zinan
%A Gao, Xin
%A Pei, Qizhi
%A Pan, Zhuoshi
%A Cai, Mengzhang
%A Wu, Jiang
%A He, Conghui
%A Wu, Lijun
%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 tang-etal-2025-middo
%X Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce **Middo**, a self-evolving **M**odel-**i**nformed **d**ynamic **d**ata **o**ptimization framework that uses model-aware data selection and context-preserving data refinement. Unlike conventional one-off filtering/synthesis methods, our framework establishes a closed-loop optimization system: (1) A self-referential diagnostic module proactively identifies suboptimal samples through tri-axial model signals - *loss patterns (complexity)*, *embedding cluster dynamics (diversity)*, and *self-alignment scores (quality)*; (2) An adaptive optimization engine then transforms suboptimal samples into pedagogically valuable training points while preserving semantic integrity; (3) This optimization process continuously evolves with model capability through dynamic learning principles. Experiments on multiple benchmarks demonstrate that our consistently enhances the quality of seed data and boosts LLM’s performance with improving accuracy by 7.15% on average while maintaining the original dataset scale. This work establishes a new paradigm for sustainable LLM training through dynamic human-AI co-evolution of data and models.
%U https://aclanthology.org/2025.emnlp-main.350/
%P 6882-6902
Markdown (Informal)
[Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning](https://aclanthology.org/2025.emnlp-main.350/) (Tang et al., EMNLP 2025)
ACL
- Zinan Tang, Xin Gao, Qizhi Pei, Zhuoshi Pan, Mengzhang Cai, Jiang Wu, Conghui He, and Lijun Wu. 2025. Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6882–6902, Suzhou, China. Association for Computational Linguistics.