Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning

TL;DR

This study uses reinforcement learning to explore how language influences children's number learning, revealing that explicit instructions and curriculum design improve learning efficiency and generalization.

Contribution

It introduces a reinforcement learning framework to model number learning in children, emphasizing the impact of linguistic instructions and curriculum strategies.

Findings

Explicit action guidance enhances learning efficiency.

Curriculum ordering accelerates convergence and improves generalization.

Language and multi-modal signals are crucial in numerical cognition.

Abstract

In this paper, we build a reinforcement learning framework to study how children compose numbers using base-ten blocks. Studying numerical cognition in toddlers offers a powerful window into the learning process itself, because numbers sit at the intersection of language, logic, perception, and culture. Specifically, we utilize state of the art (SOTA) reinforcement learning algorithms and neural network architectures to understand how variations in linguistic instructions can affect the learning process. Our results also show that instructions providing explicit action guidance are a more effective learning signal for RL agents to construct numbers. Furthermore, we identify an effective curriculum for ordering numerical-composition examples during training, resulting in faster convergence and improved generalization to unseen data. These findings highlight the role of language and multi-modal signals in numerical cognition and provide hypotheses for designing effective instructional strategies for early childhood education.