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This research demonstrates that augmenting textual problem descriptions with visual representations significantly enhances the performance of vision-language models on complex combinatorial optimization tasks like vehicle routing and job shop scheduling. By leveraging spatial structures often lost in pure text serialization, the proposed VLM achieves superior solution quality without requiring gold-standard solutions during training. This advancement highlights a critical frontier in machine learning where multimodal inputs directly improve algorithmic efficiency for hard computational problems.
VLM Fine-Tuning for End-to-End Combinatorial Optimization
arXiv:2609.37175v1 Announce Type: new 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.
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.
Computation and Language (cs.CL)
arXiv:2609.37175 [cs.CL]
(or arXiv:2609.37175v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.37175 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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