[2410.08334] Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning

Computer Science > Computation and Language arXiv:2410.08334 (cs) [Submitted on 10 Oct 2024 (v1), last revised 8 Apr 2026 (this version, v2)] Title:Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning Authors:Tirthankar Mittra View a PDF of the paper titled Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning, by Tirthankar Mittra View PDF HTML (experimental) 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 ...