# HuCurl: Human-induced Curriculum Discovery

arXiv: https://arxiv.org/abs/2307.07412 · HTML: https://mlsanity.com/paper/2307.07412/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [When Do Curricula Work?](https://mlsanity.com/paper/2012.03107/) (2020)
- [UnNatural Language Inference](https://mlsanity.com/paper/2101.00010/) (2020)
- [Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/1905.05816/) (2019)
- [Curriculum Dropout](https://mlsanity.com/paper/1703.06229/) (2017)
- [Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability](https://mlsanity.com/paper/1906.07601/) (2019)
- [Dynamic Data Selection for Curriculum Learning via Ability Estimation](https://mlsanity.com/paper/2011.00080/) (2020)
- [An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models](https://mlsanity.com/paper/2203.09486/) (2022)
- [Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits](https://mlsanity.com/paper/2110.06997/) (2021)
- [Curriculum Learning for Multi-Task Classification of Visual Attributes](https://mlsanity.com/paper/1708.08728/) (2017)
- [Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction](https://mlsanity.com/paper/1905.07791/) (2019)
- [Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks](https://mlsanity.com/paper/2205.08625/) (2022)

## Built on by (most cited first, 1 of 1)

- [Four Quadrants of Difficulty: A Simple Categorisation and its Limits](https://mlsanity.com/paper/2601.01488/) (2026)

## Abstract (arXiv)

We introduce the problem of curriculum discovery and describe a curriculum learning framework capable of discovering effective curricula in a curriculum space based on prior knowledge about sample difficulty. Using annotation entropy and loss as measures of difficulty, we show that (i): the top-performing discovered curricula for a given model and dataset are often non-monotonic as opposed to monotonic curricula in existing literature, (ii): the prevailing easy-to-hard or hard-to-easy transition curricula are often at the risk of underperforming, and (iii): the curricula discovered for smaller datasets and models perform well on larger datasets and models respectively. The proposed framework encompasses some of the existing curriculum learning approaches and can discover curricula that outperform them across several NLP tasks.