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arXiv · 2603.14550

Learning to Order: Task Sequencing as In-Context Optimization

Jan Kobiolka·Christian Frey·Arlind Kadra·Gresa Shala·Josif Grabocka
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Abstract

Task sequencing (TS) is one of the core open problems in Deep Learning, arising in a plethora of real-world domains, from robotic assembly lines to autonomous driving. Unfortunately, prior work has not convincingly demonstrated the generalization ability of meta-learned TS methods to solve new TS problems, given few initial demonstrations. In this paper, we demonstrate that deep neural networks can meta-learn over an infinite prior of synthetically generated TS problems and achieve a few-shot generalization. We meta-learn a transformer-based architecture over datasets of sequencing trajectories generated from a prior distribution that samples sequencing problems as paths in directed graphs. In a large-scale experiment, we provide ample empirical evidence that our meta-learned models discover optimal task sequences significantly quicker than non-meta-learned baselines.

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Jan Kobiolka, Christian Frey, Arlind Kadra, Gresa Shala, Josif Grabocka. 2026-03-15. Learning to Order: Task Sequencing as In-Context Optimization. https://arxiv.org/abs/2603.14550

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