MATH Benchmark — AI Glossary

Reference details

Related terms

• GSM8K
• Reasoning (LLM)
• Chain of Thought (CoT)

Example: Right answer, wrong reasoning, full marks

Grading is on the final answer, so a model that reaches the correct value through invalid steps scores identically to one that reasons soundly — and on multiple-choice or small-integer answers, a lucky guess is indistinguishable from a solution. This is why process supervision exists: grading each step rather than the endpoint separates the two, and produces a reward signal that does not credit accidental correctness. When you read a result on a maths benchmark, the question that determines what it means is whether the steps were checked or only the answer.

Commonly confused with

Competition mathematics is not applied numeracy. Strong performance here says little about arithmetic on messy real data, unit handling, or reading figures out of a document — and a model that solves olympiad-style problems can still be unreliable adding a column of numbers, because that path runs through tokenization rather than reasoning.

When to use it

Reach for it when:

• Comparing reasoning-trained models, where extended deliberation is the variable of interest
• Measuring whether chain-of-thought or process supervision helped a specific model
• As one input among several on symbolic reasoning ability

Reach for something else when:

• Predicting reliability on business arithmetic or spreadsheet-style tasks
• Without checking contamination — problem sets of this kind are widely republished
• Reading final-answer accuracy as evidence of valid reasoning

Primary source

• Hendrycks et al., Measuring Mathematical Problem Solving With the MATH Dataset (arXiv)

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