@inproceedings{li-etal-2025-teaching,
title = "Teaching According to Talents! Instruction Tuning {LLM}s with Competence-Aware Curriculum Learning",
author = "Li, Yangning and
Lu, Tingwei and
Li, Yinghui and
Chen, Yankai and
Huang, Wei-Chieh and
Jiang, Wenhao and
Wang, Hui and
Zheng, Hai-Tao and
Yu, Philip S.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.629/",
pages = "11724--11741",
ISBN = "979-8-89176-335-7",
abstract = "Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, **C**ompetence-**A**ware **M**ulti-**P**erspective c**U**rriculum in**S**truction tuning framework termed **CAMPUS** is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning."
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<abstract>Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, **C**ompetence-**A**ware **M**ulti-**P**erspective c**U**rriculum in**S**truction tuning framework termed **CAMPUS** is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.</abstract>
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%0 Conference Proceedings
%T Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning
%A Li, Yangning
%A Lu, Tingwei
%A Li, Yinghui
%A Chen, Yankai
%A Huang, Wei-Chieh
%A Jiang, Wenhao
%A Wang, Hui
%A Zheng, Hai-Tao
%A Yu, Philip S.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-teaching
%X Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, **C**ompetence-**A**ware **M**ulti-**P**erspective c**U**rriculum in**S**truction tuning framework termed **CAMPUS** is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.
%U https://aclanthology.org/2025.findings-emnlp.629/
%P 11724-11741
Markdown (Informal)
[Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning](https://aclanthology.org/2025.findings-emnlp.629/) (Li et al., Findings 2025)
ACL
- Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, and Philip S. Yu. 2025. Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 11724–11741, Suzhou, China. Association for Computational Linguistics.