[Submitted on 15 Sep 2026]
Title:RAF-VLA: Representation Alignment with the Future for End-to-End Autonomous Driving
View a PDF of the paper titled RAF-VLA: Representation Alignment with the Future for End-to-End Autonomous Driving, by Dogun Kim and 6 other authors
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Abstract:Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving actions, demonstrating strong planning performance. Future driving scenes are utilized as dense supervision, encouraging the policy to learn rich internal representations useful for planning. However, these World-Modeling VLAs rely on explicit future generation to learn such representations, thereby introducing two key limitations: additional training burden and inference latency. To address these limitations, we propose RAF-VLA (Representation Alignment with the Future), a VLA-based autonomous driving framework that shapes planning-relevant internal representations through direct guidance from future-frame representations. RAF-VLA employs Future-Aligned Supervised Fine-Tuning, in which a straightforward regularization aligns the policy's hidden states with future-frame representations obtained from a pretrained world encoder while learning driving actions. This simple alignment allows RAF-VLA to avoid the training burden and inference latency associated with future generation. Extensive experiments on the NAVSIM benchmark show that RAF-VLA achieves competitive planning performance against state-of-the-art VLA planners with substantially fewer training samples seen. Moreover, RAF-VLA incurs only 3.8% training overhead and a negligible 1 ms inference overhead.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.17728 [cs.RO]
(or arXiv:2609.17728v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.17728
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dogun Kim [view email] [v1] Tue, 15 Sep 2026 18:39:00 UTC (1,037 KB)
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