Realistic and diverse traffic simulation is essential to autonomous driving development. Yet prevailing benchmarks predominantly reward realism, and recent methods have optimized accordingly, leaving diversity underexplored. We introduce Flow-ERD, a multi-agent simulator that pursues realism and diversity jointly. Its backbone, Agent-Type Aware Flow Matching (AFM), couples flow matching's multi-modal expressiveness with type-specific kinematic execution. It preserves fine-grained diversity while keeping motions consistent with each agent type. A second stage, Entropy-Regularized Distillation (ERD), fine-tunes the closed-loop rollout distribution with an entropy-regularized reverse-KL objective. This mitigates closed-loop covariate shift while explicitly preventing collapse onto high-density modes. We evaluate Flow-ERD with Cross-Pair Diversity and its validity-gated variant alongside standard realism scores. Validation experiments further show that tuning entropy regularization recovers most of the pretrained backbone's diversity while retaining realism gains. Flow-ERD ranks first in test-split realism on the Waymo Open Sim Agents Challenge (WOSAC) and dominates the validation realism--diversity Pareto front among reproducible baselines.

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