VLM Fine-Tuning for End-to-End Combinatorial Optimization
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Computer Science > Computation and Language
arXiv:2609.37175 (cs)
[Submitted on 29 Sep 2026]
Title:VLM Fine-Tuning for End-to-End Combinatorial Optimization
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Abstract:Large language models (LLMs) have provided a unified interface for end-to-end combinatorial optimization (CO), but textual serialization alone may obscure spatial and relational structures that are important for generating effective CO solutions. This paper presents a general-purpose vision-language solver that augments textual instance descriptions with input-derived visual representations. A single vision-language model (VLM) is applied across different CO tasks and trained using supervised fine-tuning followed by verifier-guided reinforcement learning. While the visual inputs contain no gold solutions or solution-derived information, our experiments show that the VLM generally improves solution quality over its text-only counterpart, with particularly clear gains on more complex CO problems such as CVRP and JSSP. The advantage of visual information is more pronounced at large problem scales.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.37175 [cs.CL] |
| (or arXiv:2609.37175v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.37175
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled VLM Fine-Tuning for End-to-End Combinatorial Optimization, by Qingsong Yan and 5 other authors
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