# Dynamic Data Selection for Curriculum Learning via Ability Estimation

arXiv: https://arxiv.org/abs/2011.00080 · HTML: https://mlsanity.com/paper/2011.00080/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Comparing Test Sets with Item Response Theory](https://mlsanity.com/paper/2106.00840/) (2021)
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- [CLIMB: Curriculum Learning for Infant-inspired Model Building](https://mlsanity.com/paper/2311.08886/) (2023)
- [py-irt: A Scalable Item Response Theory Library for Python](https://mlsanity.com/paper/2203.01282/) (2022)
- [Can Transformer Language Models Predict Psychometric Properties?](https://mlsanity.com/paper/2106.06849/) (2021)
- [A Psychology-based Unified Dynamic Framework for Curriculum Learning](https://mlsanity.com/paper/2408.05326/) (2024)
- [Predicting Human Psychometric Properties Using Computational Language Models](https://mlsanity.com/paper/2205.06203/) (2022)
- [Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU](https://mlsanity.com/paper/2210.12499/) (2022)
- [Ling-CL: Understanding NLP Models through Linguistic Curricula](https://mlsanity.com/paper/2310.20121/) (2023)
- [Complexity-Guided Curriculum Learning for Text Graphs](https://mlsanity.com/paper/2311.13472/) (2023)
- [Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching](https://mlsanity.com/paper/2406.13361/) (2024)
- [Computational modeling of semantic change](https://mlsanity.com/paper/2304.06337/) (2023)
- [Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach](https://mlsanity.com/paper/2307.08859/) (2023)
- [HuCurl: Human-induced Curriculum Discovery](https://mlsanity.com/paper/2307.07412/) (2023)
- [PATCH! {P}sychometrics-{A}ssis{T}ed Ben{CH}marking of Large Language Models against Human Populations: A Case Study of Proficiency in 8th Grade Mathematics](https://mlsanity.com/paper/2404.01799/) (2024)
- [Capacity-Dependent Effects of Data Selection for Reasoning](https://mlsanity.com/paper/2608.13721/) (2026)
- [H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2311.11981/) (2023)
- [On Evaluation of Vision Datasets and Models using Human Competency Frameworks](https://mlsanity.com/paper/2409.04041/) (2024)
- [Learning to Solve Complex Problems via Dataset Decomposition](https://mlsanity.com/paper/2602.20296/) (2026)
- [Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy](https://mlsanity.com/paper/2607.18984/) (2026)

## Abstract (arXiv)

Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks.