AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
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 p...
| Authors | Shengda Gu, Kai Li, Xinyi Ke et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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