A tiny model can learn math-like symbol transformations
MathFormer is a small with 4 million parameters. Without built-in math knowledge, it reached about 98.6% accuracy on symbolic math tasks that turn factored expressions into expanded expressions.
For example, it receives an expression with parentheses and predicts the multiplied-out form. The result suggests the model may be learning structured token transformations rather than understanding what operators or variables mean.
This supports the idea that larger may sometimes appear to do mathematical reasoning while actually completing large-scale structured patterns. One open question is how changes this behavior when the core architecture still relies on attention.
Key points
- A 4 million-parameter reached about 98.6% accuracy on symbolic math tasks.
- The task was to convert factored expressions into expanded expressions.
- The result suggests structured token transformation may explain some math-like behavior.
- It raises a question about whether are reasoning or completing learned patterns in some cases.
- Agent builders may test small specialized models for repeatable sub-tasks to lower .