Why do fixed benchmarks fail as agents get stronger?

The Red Queen Gödel Machine names three failure modes of stationary evaluation in self-improving search: some target tasks have no direct benchmark, some evaluation is slow or weakly informative, and — most sharply — static benchmarks saturate or become vulnerable to reward hacking as agents improve. The evolutionary analogy is the argument: species do not optimize against a frozen environment, they adapt as competitors adapt in turn. A fixed verifier is a frozen environment, and an improving agent will eventually learn the verifier's blind spots rather than the underlying task.

This directly parallels Does self-consistency reliably reward correct answers during training?: once a signal is fixed and the optimizer is strong enough, Goodhart's Law converts the proxy into a target and the correlation that made it useful degrades. RQGM's answer is structural rather than a patch — controlled utility evolution splits search into epochs with a fixed within-epoch criterion, so the standard self-improvement guarantees still apply per epoch, but the utility updates at each epoch boundary. Therefore the objective can be hardened faster than the agent can game it, because the target moves. The design lesson generalizes beyond RQGM: any long-running optimizer against a fixed reward is on a countdown to reward hacking, and the fix is not a better static reward but a reward that co-adapts on a controlled schedule.