As the foundation of closed-loop training and evaluation in autonomous driving, traffic simulation still faces two fundamental challenges: covariate shift introduced by open-loop imitation learning and limited capacity to reflect the multimodal behaviors observed in real-world traffic. Although recent frameworks such as RIFT have partially addressed these issues through group-relative optimization, their forward simulation procedures remain largely non-reactive, leading to unrealistic agent interactions within the virtual domain and ultimately limiting simulation fidelity. To address these issues, we propose ForSim, a stepwise closed-loop forward simulation paradigm. At each virtual timestep, the traffic agent propagates the virtual candidate trajectory that best spatiotemporally matches the reference trajectory through physically grounded motion dynamics, thereby preserving multimodal behavioral diversity while ensuring intra-modality consistency. Other agents are updated with stepwise predictions, yielding coherent and interaction-aware evolution. When incorporated into the RIFT traffic simulation framework, ForSim operates in conjunction with group-relative optimization to fine-tune traffic policy. Extensive experiments confirm that this integration consistently improves safety while maintaining efficiency, realism, and comfort. These results underscore the importance of modeling closed-loop multimodal interactions within forward simulation and enhance the fidelity and reliability of traffic simulation for autonomous driving. Project Page: https://currychen77.github.io/ForSim/

翻译：作为自动驾驶闭环训练与评估的基础，交通仿真仍面临两个根本性挑战：由开环模仿学习引入的协变量偏移，以及反映现实交通中观察到的多模态行为的能力有限。尽管近期如RIFT等框架通过群体相对优化部分解决了这些问题，但其前向仿真过程仍基本不具备反应性，导致虚拟域内智能体交互不真实，最终限制了仿真保真度。为解决这些问题，我们提出了ForSim，一种逐步闭环前向仿真范式。在每个虚拟时间步，交通智能体通过基于物理的运动动力学传播时空匹配参考轨迹最优的虚拟候选轨迹，从而在保持模态内一致性的同时保留多模态行为多样性。其他智能体通过逐步预测进行更新，产生连贯且具有交互意识的演化。当整合至RIFT交通仿真框架时，ForSim与群体相对优化协同工作以微调交通策略。大量实验证实，该集成能持续提升安全性，同时保持效率、真实性与舒适性。这些结果凸显了在前向仿真中建模闭环多模态交互的重要性，并增强了自动驾驶交通仿真的保真度与可靠性。项目页面：https://currychen77.github.io/ForSim/