# An Optimization Framework for Task Sequencing in Curriculum Learning

arXiv: https://arxiv.org/abs/1901.11478 · HTML: https://mlsanity.com/paper/1901.11478/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [Transferable Curricula through Difficulty Conditioned Generators](https://mlsanity.com/paper/2306.13028/) (2023)
- [Teacher-student curriculum learning for reinforcement learning](https://mlsanity.com/paper/2210.17368/) (2022)
- [Automaton-Guided Curriculum Generation for Reinforcement Learning Agents](https://mlsanity.com/paper/2304.05271/) (2023)
- [Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning](https://mlsanity.com/paper/2403.17266/) (2024)
- [Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion](https://mlsanity.com/paper/2408.09838/) (2024)
- [Uncovering Intrinsic Capabilities: A Paradigm for Data Curation in Vision-Language Models](https://mlsanity.com/paper/2510.00040/) (2025)
- [Curricular Transfer Learning for Sentence Encoded Tasks](https://mlsanity.com/paper/2308.01849/) (2023)
- [Anticipating the Consequences of Curriculum Decisions with Large Language Models](https://mlsanity.com/paper/2610.04604/) (2026)

## Abstract (arXiv)

Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level. We propose novel uses of curriculum learning, which arise from choosing different objective functions. Furthermore, we define a general optimization framework for task sequencing and evaluate the performance of popular metaheuristic search methods on several tasks. We show that curriculum learning can be successfully used to: improve the initial performance, take fewer suboptimal actions during exploration, and discover better policies.