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    <titleInfo>
        <title>A Psychology-based Unified Dynamic Framework for Curriculum Learning</title>
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    <name type="personal">
        <namePart type="given">Guangyu</namePart>
        <namePart type="family">Meng</namePart>
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        <namePart type="given">Qingkai</namePart>
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    <name type="personal">
        <namePart type="given">Hong</namePart>
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    <abstract>Directly learning from examples of varying difficulty levels is often challenging for both humans and machine learning models. A more effective strategy involves exposing learners to examples in a progressive order from easy to difficult. Curriculum Learning (CL) has been proposed to implement this strategy in machine learning model training. However, two key challenges persist in CL framework design: defining the difficulty of training data and determining the appropriate amount of data to input at each training step. Drawing inspiration from psychometrics, this article presents a Psychology-based Unified Dynamic Framework for Curriculum Learning (PUDF). We quantify the difficulty of training data by applying Item Response Theory (IRT) to responses from Artificial Crowds (AC). This theory-driven IRT-AC approach leads to global (i.e., model-independent) and interpretable difficulty values. Leveraging IRT, we propose a training strategy, Dynamic Data Selection via Model Ability Estimation (DDS-MAE), to schedule the appropriate amount of data during model training. Because our difficulty labeling and model ability estimation are based on a consistent theory, namely, IRT, their values are comparable within the same scope, potentially leading to aligned training data selection and faster convergence compared with the other CL methods. Experimental results demonstrate that fine-tuning pre-trained large language models with PUDF leads to higher accuracy and faster convergence on a suite of benchmark datasets compared with standard fine-tuning and state-of-the-art CL methods. Ablation studies and downstream analyses further validate the impact of PUDF for CL.</abstract>
    <identifier type="citekey">meng-etal-2026-psychology</identifier>
    <identifier type="doi">10.1162/coli.a.584</identifier>
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        <url>https://aclanthology.org/2026.cl-3.4/</url>
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        <date>2026-09</date>
        <detail type="volume"><number>52</number></detail>
        <detail type="issue"><number>3</number></detail>
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            <start>1001</start>
            <end>1049</end>
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%0 Journal Article
%T A Psychology-based Unified Dynamic Framework for Curriculum Learning
%A Meng, Guangyu
%A Zeng, Qingkai
%A Lalor, John P.
%A Yu, Hong
%J Computational Linguistics
%D 2026
%8 September
%V 52
%N 3
%I MIT Press
%C Cambridge, MA
%F meng-etal-2026-psychology
%X Directly learning from examples of varying difficulty levels is often challenging for both humans and machine learning models. A more effective strategy involves exposing learners to examples in a progressive order from easy to difficult. Curriculum Learning (CL) has been proposed to implement this strategy in machine learning model training. However, two key challenges persist in CL framework design: defining the difficulty of training data and determining the appropriate amount of data to input at each training step. Drawing inspiration from psychometrics, this article presents a Psychology-based Unified Dynamic Framework for Curriculum Learning (PUDF). We quantify the difficulty of training data by applying Item Response Theory (IRT) to responses from Artificial Crowds (AC). This theory-driven IRT-AC approach leads to global (i.e., model-independent) and interpretable difficulty values. Leveraging IRT, we propose a training strategy, Dynamic Data Selection via Model Ability Estimation (DDS-MAE), to schedule the appropriate amount of data during model training. Because our difficulty labeling and model ability estimation are based on a consistent theory, namely, IRT, their values are comparable within the same scope, potentially leading to aligned training data selection and faster convergence compared with the other CL methods. Experimental results demonstrate that fine-tuning pre-trained large language models with PUDF leads to higher accuracy and faster convergence on a suite of benchmark datasets compared with standard fine-tuning and state-of-the-art CL methods. Ablation studies and downstream analyses further validate the impact of PUDF for CL.
%R 10.1162/coli.a.584
%U https://aclanthology.org/2026.cl-3.4/
%U https://doi.org/10.1162/coli.a.584
%P 1001-1049

Markdown (Informal)

[A Psychology-based Unified Dynamic Framework for Curriculum Learning](https://aclanthology.org/2026.cl-3.4/) (Meng et al., CL 2026)

• A Psychology-based Unified Dynamic Framework for Curriculum Learning (Meng et al., CL 2026)

ACL

• Guangyu Meng, Qingkai Zeng, John P. Lalor, and Hong Yu. 2026. A Psychology-based Unified Dynamic Framework for Curriculum Learning. Computational Linguistics, 52(3):1001–1049.