Math Reasoning Evaluation
Checking both the intermediate logical steps and the final numeric answer of a model’s reasoning.
Math reasoning evaluation grades a model's work the way a teacher grades a proof, not just an answer key: it checks whether each intermediate step follows logically from the last, in addition to whether the final number is correct. A model can arrive at the right final answer through flawed reasoning (a lucky arithmetic error canceling an earlier mistake), and a rigorous evaluation flags that as a failure even though the answer alone looks right.
This distinction matters because reasoning evaluations are what actually surface chain-of-thought quality — models trained only against final-answer correctness can learn to guess well without reasoning soundly, which shows up as brittleness on problems that are structurally similar but numerically different.
Annotators for this task generally need enough domain fluency to independently verify each step, not just recognize a plausible-looking derivation.
What this means for trainers
Verify every step independently rather than skimming for a final answer that matches your own quick mental math — a correct final number with broken intermediate logic should still be marked as a failure.
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