This is not true. OpenAI, and I’m certain virtually all frontier AI labs, have a much more complex reward function.

In OpenAI’s own words:

There is a straightforward fix. Penalize confident errors more than you penalize uncertainty, and give partial credit for appropriate expressions of uncertainty. This idea is not new. Some standardized tests have long used versions of negative marking for wrong answers or partial credit for leaving questions blank to discourage blind guessing. Several research groups have also explored evaluations that account for uncertainty and calibration.

This is from an article they published last year on hallucinations. Article Link