Original title: Rethinking Learnability in Offline Data-driven Optimization
Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue · September 2026
Finding the best design can be slow and expensive. In offline data-driven optimization, computers try to locate top solutions using only a fixed log of past attempts without running new tests. However, general machine learning guarantees fail here because a model can be accurate almost everywhere while completely misunderstanding the best area. To solve this, researchers introduced algorithm-dependent learnability. Instead of learning the entire problem space, an algorithm only needs to be accurate along its own search path. The researchers showed this idea works theoretically for several search strategies in discrete and continuous tasks. Next, they built a method called Uncertainty-aware Gradient-guided Trajectory Learning, or UGTL. UGTL traces out plausible paths of improvement, models these paths using conditional diffusionA type of machine learning model that generates data by gradually removing noise, guided by specific conditions., and picks a diverse set of candidates. When tested across five Design-Bench tasks, UGTL earned the best aggregate mean rank of 3.1/25 among 25 competing methods.
Imagine you're trying to find the fastest route through a city with many streets. You have a map from last year, but roads change, and you can't drive around to test each one now. The hard part is that the map might be outdated for many streets, but you don't need to know all of them—you only need to know the ones you'd actually use. In this paper, offline optimization uses old data to find the best solution without new tests. The researchers show that instead of learning the entire city map, you only need to learn the specific path you'd take, which is much easier.
When companies use past data to improve designs without new experiments, efficiency matters. This work shows that focusing learning on the search path, not the entire landscape, is theoretically sufficient. That could save resources and make optimization more reliable in practice.
The theoretical results are proven only for greedy/local search in submodular maximization and projected gradient descent in convex minimization. Experiments are conducted on five Design-Bench tasks, so broader applicability is not established. The abstract does not address computational costs or comparisons with all existing methods.
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Rethinking Learnability in Offline Data-driven Optimization — Chao Qian, Chen-Guang Wang, Rong-Xi Tan et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.