When a model can produce a long, confident, well-structured chain of reasoning, a non-expert rater has almost no way to tell whether it is right. Fluency masks error, and evaluation quietly becomes a coin flip.
Where generic rating fails
On reasoning tasks, the failure mode is systematic: plausible-but-wrong answers score well, and genuine subtle correctness goes unrewarded. You end up optimising for confidence, not correctness.
Evaluate the steps, not just the answer
A practical fix is to score the reasoning, not only the final answer. A model can reach the right conclusion through flawed logic, or a wrong conclusion through mostly-sound steps. Asking experts to check the chain, is each step valid, is the cited fact real, does the conclusion follow, catches failures that a right/wrong label on the final answer would miss entirely.
What to do instead
Use domain experts who can actually verify the reasoning, and record that they did. Provenance keeps the human signal honest, and per-batch trust catches raters who are guessing rather than checking.
Beware reward-hacking your own evals
If your evaluation rewards confident, fluent, well-formatted answers, models will learn to be confident, fluent and well-formatted, whether or not they are right. Expert evaluation that rewards correct reasoning, not polish, is the guard against optimising your model into a persuasive but unreliable state.
Evaluate with experts
Pathwize provides credential-verified experts for reasoning evaluation, with recorded oversight. Book a demo to scope it for your evals.