@inproceedings{zhang-liu-2026-learning,
title = "Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for {LLM} Supervised Post-Training",
author = "Zhang, Mengxiang and
Liu, Lingyuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.505/",
pages = "11029--11045",
ISBN = "979-8-89176-390-6",
abstract = "Supervised post-training is essential for refining Large Language Models (LLMs), yet its effectiveness relies heavily on strategic data curation. Traditional Curriculum Learning (CL) strategies often fail to account for the evolving proficiency of the learner, relying instead on static, single dimensional metrics. We propose EVO-Curate, a dynamic data curation framework that synchronizes sample complexity with the maturing capacity of the LLM. EVO-Curate employs an Adaptive Dynamics Measurer to synthesize instantaneous difficulty and historical variability into a multidimensional utility score. To maintain representational diversity, we introduce an Evolutionary Sampling Scheduler based on an entropy maximizing mechanism. Empirical evaluations across instruction following, mathematical reasoning, and code generation demonstrate that EVO-Curate consistently outperforms standard training baselines and traditional CL methods across various architectures and scales. Specifically, our framework achieves relative performance gains of up to about 10{\%} while maintaining manageable computational overhead. These results establish EVO-Curate as a scalable and model agnostic solution for enhancing the efficiency of modern LLM training pipelines."
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<abstract>Supervised post-training is essential for refining Large Language Models (LLMs), yet its effectiveness relies heavily on strategic data curation. Traditional Curriculum Learning (CL) strategies often fail to account for the evolving proficiency of the learner, relying instead on static, single dimensional metrics. We propose EVO-Curate, a dynamic data curation framework that synchronizes sample complexity with the maturing capacity of the LLM. EVO-Curate employs an Adaptive Dynamics Measurer to synthesize instantaneous difficulty and historical variability into a multidimensional utility score. To maintain representational diversity, we introduce an Evolutionary Sampling Scheduler based on an entropy maximizing mechanism. Empirical evaluations across instruction following, mathematical reasoning, and code generation demonstrate that EVO-Curate consistently outperforms standard training baselines and traditional CL methods across various architectures and scales. Specifically, our framework achieves relative performance gains of up to about 10% while maintaining manageable computational overhead. These results establish EVO-Curate as a scalable and model agnostic solution for enhancing the efficiency of modern LLM training pipelines.</abstract>
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%0 Conference Proceedings
%T Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for LLM Supervised Post-Training
%A Zhang, Mengxiang
%A Liu, Lingyuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-liu-2026-learning
%X Supervised post-training is essential for refining Large Language Models (LLMs), yet its effectiveness relies heavily on strategic data curation. Traditional Curriculum Learning (CL) strategies often fail to account for the evolving proficiency of the learner, relying instead on static, single dimensional metrics. We propose EVO-Curate, a dynamic data curation framework that synchronizes sample complexity with the maturing capacity of the LLM. EVO-Curate employs an Adaptive Dynamics Measurer to synthesize instantaneous difficulty and historical variability into a multidimensional utility score. To maintain representational diversity, we introduce an Evolutionary Sampling Scheduler based on an entropy maximizing mechanism. Empirical evaluations across instruction following, mathematical reasoning, and code generation demonstrate that EVO-Curate consistently outperforms standard training baselines and traditional CL methods across various architectures and scales. Specifically, our framework achieves relative performance gains of up to about 10% while maintaining manageable computational overhead. These results establish EVO-Curate as a scalable and model agnostic solution for enhancing the efficiency of modern LLM training pipelines.
%U https://aclanthology.org/2026.acl-long.505/
%P 11029-11045
Markdown (Informal)
[Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for LLM Supervised Post-Training](https://aclanthology.org/2026.acl-long.505/) (Zhang & Liu, ACL 2026)
ACL