Author

Peter Karkus

National University of Singapore

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#machine learning Preprint Sep 2026

OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher

As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end driving policies are pre-trained in open loop with behavior cloning on human demonstrations. However, compounding errors during closed-loop de...

D. Da Col, M. Igl, Peter Karkus et al. · 1 citation

#artificial intelligence Jun 2026

Planning-aligned Token Compression for Long-Context Autonomous Driving

This work proposes COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE, compressing extended context into bounded representations, and evaluates on high-signal dynamic scenarios where historical context is most critical for behavior correctness, and accordingly design behavioral metrics.

Zhixuan Liang, Yuxiao Chen, Yurong You et al. · arXiv.org · 1 citation