Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning

• URL: http://arxiv.org/abs/2509.23993v1
• Date: Sun, 28 Sep 2025 17:36:13 GMT
• Title: Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning
• Authors: Muleilan Pei, Shaoshuai Shi, Shaojie Shen,
• Abstract summary: We propose a novel R1-style reinforcement fine-tuning paradigm tailored for next-token prediction models to better align agent behavior with human preferences and evaluation metrics.<n>Our approach introduces a metric-oriented policy optimization algorithm to improve distribution alignment and an iterative "SFT-RFT-SFT" training strategy that alternates between Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT)<n>The results on the Open Sim Agents Challenge showcase that SMART-R1 achieves state-of-the-art performance with an overall realism meta score of 0.7858, ranking first on
• Score: 35.83999932977034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scalable and realistic simulation of multi-agent traffic behavior is critical for advancing autonomous driving technologies. Although existing data-driven simulators have made significant strides in this domain, they predominantly rely on supervised learning to align simulated distributions with real-world driving scenarios. A persistent challenge, however, lies in the distributional shift that arises between training and testing, which often undermines model generalization in unseen environments. To address this limitation, we propose SMART-R1, a novel R1-style reinforcement fine-tuning paradigm tailored for next-token prediction models to better align agent behavior with human preferences and evaluation metrics. Our approach introduces a metric-oriented policy optimization algorithm to improve distribution alignment and an iterative "SFT-RFT-SFT" training strategy that alternates between Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT) to maximize performance gains. Extensive experiments on the large-scale Waymo Open Motion Dataset (WOMD) validate the effectiveness of this simple yet powerful R1-style training framework in enhancing foundation models. The results on the Waymo Open Sim Agents Challenge (WOSAC) showcase that SMART-R1 achieves state-of-the-art performance with an overall realism meta score of 0.7858, ranking first on the leaderboard at the time of submission.

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