Instance-Specific Language Models Enhance Combinatorial Optimization

Researchers have developed LM-GRASP, a novel framework that uses instance-specific language models for combinatorial optimization tasks. This approach reformulates the construction phase of the GRASP metaheuristic as an online imitation learning problem, training a decoder-only Transformer model from scratch for each specific problem instance. Unlike traditional methods that rely on static heuristic rules, LM-GRASP learns from high-quality solutions discovered during its own search process, eliminating the need for problem-specific feature engineering. Evaluations on the Taillard PFSP benchmark showed LM-GRASP outperforming GPU-GRASP by an average of 28.4 makespan units, indicating the potential of online-trained language models as a practical alternative to hand-engineered constructors. AI

IMPACT This approach could lead to more efficient and adaptable solutions for complex optimization problems across various industries.

RANK_REASON The cluster describes a new research paper detailing a novel method for combinatorial optimization using language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• GPU-GRASP
• Hugging Face
• LM-GRASP
• Taillard PFSP
• Transformer++

• paper
• other

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