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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

This work proposes OPTED (on-policy fine-tuning for end-to-end driving) which decouples reinforcement learning from the post-training of the end-to-end policy: a privileged teacher is trained using RL on vectorized inputs (HD-map and bounding boxes) which provides supervision to the pre-trained student during closed-lo...

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

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. · 1 citation

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