AutoOR uses synthetic data generation and RL post-training with solver feedback to enable 8B LLMs to autoformalize linear, mixed-integer, and non-linear OR problems, matching larger models on benchmarks.
Learning to optimize for mixed-integer non-linear programming
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A hybrid L2O framework predicts optimal integer solutions for MIQP via neural network, recovers continuous variables with a differentiable QP layer, and trains with supervised optimality loss plus self-supervised feasibility loss.
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AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
AutoOR uses synthetic data generation and RL post-training with solver feedback to enable 8B LLMs to autoformalize linear, mixed-integer, and non-linear OR problems, matching larger models on benchmarks.
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A Hybrid Learning-to-Optimize Framework for Mixed-Integer Quadratic Programming
A hybrid L2O framework predicts optimal integer solutions for MIQP via neural network, recovers continuous variables with a differentiable QP layer, and trains with supervised optimality loss plus self-supervised feasibility loss.
- ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs