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This research introduces LaT, an LLM-driven training paradigm designed to mitigate bias in multi-task neural solvers for Vehicle Routing Problems. By leveraging a pretrained large language model as an external trainer that generates stage-wise guidance vectors, the method improves solution quality across diverse VRP variants without requiring costly bi-level optimization. The approach demonstrates how integrating generative models into reinforcement learning pipelines can enhance adaptability and generalization in complex combinatorial optimization tasks.
LaT: LLM-as-Trainer for Multi-Task Vehicle Routing Solvers
arXiv:2607.17708v1 Announce Type: new Abstract: Multi-task neural solvers aim to handle multiple Vehicle Routing Problem (VRP) variants within a unified model, avoiding separate training for each constraint combination. However, VRP variants differ in optimization difficulty, while existing methods lack stage-wise feedback on their training status, making the model biased to some specific variants. Although meta-learning can support adaptive training, it typically requires bi-level optimization and additional gradient updates, increasing computational cost. To address this limitation, we propose LLM-as-Trainer (LaT), a plug-and-play training paradigm that uses a pretrained large language model as an external trainer. LaT periodically analyzes cross-task validation metrics to generate a stage-wise guidance vector. This vector is combined with the current task's constraint vector and injected into each encoder layer, providing the neural solver with additional training information during subsequent policy optimization. Experiments on 16 VRP variants show that LaT improves the solution quality of several state-of-the-art multi-task neural solvers on both trained and unseen variants, supporting the effectiveness and generality of the proposed training paradigm.
Abstract:Multi-task neural solvers aim to handle multiple Vehicle Routing Problem (VRP) variants within a unified model, avoiding separate training for each constraint combination. However, VRP variants differ in optimization difficulty, while existing methods lack stage-wise feedback on their training status, making the model biased to some specific variants. Although meta-learning can support adaptive training, it typically requires bi-level optimization and additional gradient updates, increasing computational cost. To address this limitation, we propose LLM-as-Trainer (LaT), a plug-and-play training paradigm that uses a pretrained large language model as an external trainer. LaT periodically analyzes cross-task validation metrics to generate a stage-wise guidance vector. This vector is combined with the current task's constraint vector and injected into each encoder layer, providing the neural solver with additional training information during subsequent policy optimization. Experiments on 16 VRP variants show that LaT improves the solution quality of several state-of-the-art multi-task neural solvers on both trained and unseen variants, supporting the effectiveness and generality of the proposed training paradigm.
Artificial Intelligence (cs.AI)
arXiv:2607.17708 [cs.AI]
(or arXiv:2607.17708v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.17708 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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