ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations

Quang Hieu Pham; Thuy Duong Nguyen; Tung Pham; Anh Tuan Luu; Dat Quoc Nguyen

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arXiv · 2506.03763

ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations

Quang Hieu Pham·Thuy Duong Nguyen·Tung Pham·Anh Tuan Luu·Dat Quoc Nguyen

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Abstract

The capabilities of large language models (LLMs) have been enhanced by training on data that reflects human thought processes, such as the Chain-of-Thought format. However, evidence suggests that the conventional scheme of next-word prediction may not fully capture how humans learn to think. Inspired by how humans generalize mathematical reasoning, we propose a new approach named ClozeMath to fine-tune LLMs for mathematical reasoning. Our ClozeMath involves a text-infilling task that predicts masked equations from a given solution, analogous to cloze exercises used in human learning. Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses the strong baseline Masked Thought in performance and robustness, with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. Additionally, we conduct an ablation study to analyze the effects of various architectural and implementation choices on our approach.

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Quang Hieu Pham, Thuy Duong Nguyen, Tung Pham, Anh Tuan Luu, Dat Quoc Nguyen. 2025-06-04. ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations. https://arxiv.org/abs/2506.03763

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