OptClimber: Curriculum Experience Learning for LLM Optimization Modeling

Large language models have shown strong potential for automated optimization modeling, yet existing methods mainly focus on how modeling knowledge is provided through prompting or fine-tuning, while paying less attention to how such knowledge should be accumulated throughout learning. We introduce OptClimber, a curriculum experience learning framework that organizes optimization problems by domain and difficulty and progressively builds reusable modeling knowledge from solver-verified problem-solving trajectories. OptClimber couples curriculum scheduling with experience accumulation, allowing reliable modeling principles to be acquired and refined as problem complexity increases. The accumulated experiences are further used to construct solver-verified trajectories for supervised fine-tuning, while the same curriculum structure organizes subsequent reinforcement learning with solver-grounded feedback. In this way, OptClimber connects data organization, experience accumulation, supervised learning, and reinforcement learning within a unified framework. Experiments on six optimization modeling benchmarks show that the prompt-based OptClimber improves its backbone by 18.3% on average, with larger gains on challenging tasks. After fine-tuning and curriculum-guided reinforcement learning, OptClimber-14B achieves 72.7% average accuracy and surpasses the current state-of-the-art fine-tuned model.