New framework improves autonomous driving models with combined IL and RL

By PulseAugur Editorial · [1 sources] · 2026-06-16 04:00

Researchers have introduced CoIRL-AD, a novel framework for training autonomous driving models that combines imitation learning (IL) and reinforcement learning (RL) in an offline setting. This approach aims to improve generalization, particularly in rare scenarios, by decoupling IL and RL objectives and using imagined rollouts for reward estimation. Experiments on the nuScenes benchmark demonstrated that CoIRL-AD enhances robustness and cross-city generalization compared to existing IL-based methods. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for autonomous driving. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong · 2026-06-16 04:00

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving

arXiv:2510.12560v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models trained with imitation learning (IL) often generalize poorly, particularly in long-tail scenarios where expert demonstrations are sparse. Reinforcement learning (RL) can provide complem…

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CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving

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