CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (MLLMs), researchers have proposed the paradigm of Vision-Language-Action (VLA) models for E2E-AD, where it seeks to integrate visual perception, language understanding and action prediction within a single policy. However, existing VLA-based policies largely adopts imitation learning, where it only learns to drive by optimizing distance-based metrics w.r.t. logged expert trajectories. Such distribution shift between open-loop training and closed-loop inference leads to suboptimal performance in closed-loop plannin...
| Authors | Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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