arXiv:2606.05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GASING method -- an Indonesian pedagogy that solves basic arithmetic through a left-to-right procedure aligned with the causal order of token generation -- we operationalize each operation as a computational procedure whose execution trace is serialized into natural-language Chain-of-Thought (CoT) supervision. A small GPT-2 decoder (86M parameters) with a syllabic-agglutinative TOBA tokenizer for In
The continuous drive for more advanced and reliable AI systems, especially in reasoning, makes this research timely as current language models struggle with complex arithmetic.
This research suggests a fundamental shift in how language models can be trained for arithmetic reasoning, potentially leading to more robust and explainable AI in critical domains.
The operationalization of human pedagogical methods, like GASING, into verifiable computational procedures and natural-language Chain-of-Thought supervision alters the approach to AI arithmetic training.
- · AI researchers
- · Open-source AI communities
- · Educational technology developers
- · Developers relying solely on brute-force data training
- · AI models without explainable reasoning
Improved arithmetic capabilities in language models through pedagogically-inspired training methods.
Development of more transparent and auditable AI systems, especially for tasks requiring numerical accuracy.
Potential for AI to assist in creating more effective educational tools by mimicking and optimizing human learning processes.
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Read at arXiv cs.AI