Prompt optimization is an attractive alternative to reinforcement learning for adapting language-model agents, since it is cheap and produces human-readable artifacts.
On factual tasks, however, it carries a failure mode that scalar-reward reinforcement learning does not.
An optimizer that reads the training examples can write their answers into the prompt, so an in-distribution gain may be memorization rather than transferable behaviour.
We apply reflective prompt evolution (GEPA) to the two system prompts of a multi-agent deep-research agent and evaluate it under a topic-disjoint split in which no topic is shared across train, validation, and test.
Held-out citation reward rises by 0.079, from 0.702 to 0.781, with a bootstrap 95% confidence interval of [0.051, 0.107], and the gain replicates across three seeds (mean 0.107).
Nearly half of the winning prompt is a memorized answer key, yet a controlled ablation shows this content earns no reward even on the exact training questions it encodes.
The large validation-minus-held-out gap is therefore small-sample selection under a noisy judge, not memorization.
Varying the verifier isolates what makes the gain transfer.
Decomposed per-dimension feedback is the causal ingredient, and collapsing it to a single scalar reward makes held-out transfer negative.
The gain persists under an out-of-family judge and is largely reproduced by a hand-written version of the discovered recipe.
Credible evaluation of prompt optimizers therefore requires held-out topics, multiple seeds, and verifier ablations.
We release the candidate prompts, lineage diffs, figures, tables, and analysis.