Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Learning diverse and high-fidelity traffic simulations from human driving demonstrations is crucial for autonomous driving evaluation. The recent next-token prediction (NTP) paradigm, widely adopted in large language models (LLMs), has been applied to traffic simulation and achieves iterative improvements via supervised fine-tuning (SFT). However, such methods limit active exploration of potentially valuable motion tokens, particularly in suboptimal regions. Entropy patterns provide a promising perspective for enabling exploration driven by motion token uncertainty. Motivated by this insight, we propose a novel tokenized traffic simulation policy, R1Sim, which represents an initial attemp...
| Authors | Ziyan Wang, Peng Chen, Ding Li et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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