AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
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Computer Science > Machine Learning
Title:AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Abstract:Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain. Neural combinatorial optimization (NCO) learns fast construction policies, typically with reinforcement learning (RL), while preference-based NCO improves sample efficiency by learning from relative solution quality. However, existing preference objectives combine two distinct design choices in manually specified, one-size-fits-all formulations: what learning signal to extract from each solution pair and how to weight each pair relative to the sampled set. We present AutoPref, the first LLM-guided framework for automated preference-objective discovery in NCO. AutoPref factorizes the objective into a pairwise loss program, which defines the learning signal, and a set-aware weighting program, which determines each pair's relative contribution. Their composition forms a unified programmatic objective space containing existing preference objectives as special cases. To make its search tractable, we introduce a staged conditional search strategy with behavioral gates that filter inadmissible programs before short-horizon training and evaluation. Across TSP, CVRP, FFSP, and JSSP, AutoPref consistently outperforms strong hand-designed baselines across problem scales, demonstrating the benefits and scalability of automated objective discovery for NCO.
| Comments: | 8pages, 2figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.27953 [cs.LG] |
| (or arXiv:2607.27953v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.27953
arXiv-issued DOI via DataCite (pending registration)
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