This will just lead to the evaluatee finding anomalies in evaluator and exploiting them for maximum gains. It happened many times already where a ML model controled an object in a physical world simulator, and all it learned was to exploit simulator bugs [1]

[1] https://boingboing.net/2018/11/12/local-optima-r-us.html

Thats a natural tendency for optimization algorithms