Synthetic dialogue corpora are increasingly used as proxies for target data, yet persona-grounded generators optimize individual conversations rather than corpus composition, yielding plausible dialogues with distorted population-level behavior mixes. We introduce GroupPersona, a framework that aligns synthetic corpora with a reference behavior distribution. GroupPersona turns population statistics into generation controls by discovering recurring behavioral groups in the reference corpus and using their prevalence and interaction patterns to condition user agents.

We evaluate corpus-level fidelity through behavioral alignment and structural preservation, measured by average Jensen–Shannon distance over 12 behavior attributes and 5 dialogue structure attributes, respectively. Across four corpora spanning two source families and two construction regimes, GroupPersona reduces behavioral Jensen–Shannon distance from 0.234 to 0.177 over the best-average baseline, a 24.4% reduction. It is best or tied-best on all four corpora. It maintains comparable structural distance at 0.167 versus 0.169. Together, these results show that GroupPersona improves population-level behavioral fidelity while preserving dialogue structure.