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CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving

Summary

CLEAR introduces a closed-loop training system using reinforcement learning for end-to-end autonomous driving. By learning a residual waypoint policy from pretrained VLA models and employing a heterogeneous simulation pipeline, CLEAR achieves state-of-the-art performance on CARLA longest6 v2 and Bench2Drive.

SourcearXiv RoboticsAuthor: Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh, Fatih Porikli
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
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[Submitted on 3 Jul 2026]

Title:CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving

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Abstract: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 planning. To close this gap, we present CLEAR, a system that enables closed-loop training using Reinforcement Learning (RL) at scale for E2E-AD. We propose to learn a novel residual waypoint policy around the waypoint prior from pretrained VLA policies, effectively harnessing the knowledge within. On another front, one of the key challenges to scale up RL for vision-based policies is the number of parallel simulation environments since RL is data hungry. To that end, we design a heterogeneous pipeline that places the simulator and the VLA learner on distinct compute groups, which allows us to dramatically increase the number of simulation environments running in parallel while avoiding resource contention and maintaining training stability. We show that with a simple reward, CLEAR significantly outperforms previous methods and sets new state-of-the-art performance on the challenging benchmarks of CARLA longest6 v2 and Bench2Drive.

Subjects:

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.02841 [cs.RO]

(or arXiv:2607.02841v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.02841

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yunxiao Shi [view email] [v1] Fri, 3 Jul 2026 00:34:50 UTC (2,880 KB)

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Key points and analysis

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Key points

  • Closed-loop RL addresses the distribution shift in imitation learning for VLA-based autonomous driving.
  • A residual waypoint policy leverages prior knowledge from pretrained VLA models.
  • Heterogeneous pipeline scales parallel simulation environments without resource contention.
  • CLEAR sets new SOTA on CARLA longest6 v2 and Bench2Drive benchmarks.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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