mlsanityData to 14 Oct 2026

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

arXiv 2410.08334 ↗First posted 10 Oct 2024cs.CL cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 38 references that are in the map. First posted on arXiv 10 Oct 2024.

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15 of 38 references.

References · 15

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  8. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  9. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
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3 more
  1. 2020Reinforcement Learning in Economics and Financecited by 58
  2. 2021Tell me why! Explanations support learning relational and causal structurecited by 36
  3. 2020On Catastrophic Interference in Atari 2600 Gamescited by 13

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  4. 2025A Review of Developmental Interpretability in Large Language Models
  5. 2022Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic
  6. 2024From Next-Token to Mathematics: The Learning Dynamics of Mathematical Reasoning in Language Models
  7. 2025Curiosity-Driven Development of Action and Language in Robots Through Self-Exploration
  8. 2025Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles