Abstract:Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a scalable benchmark of solver-verified optimization problems spanning 12 categories, from textbook linear programs to stochastic and multi-objective formulations with up to thousands of variables. Across 10+ models, we find that accuracy collapses as instance data grows, even when the formulation itself is simple. We call this the effective binding limit. We address this via a simple inference-time approach, BIND, which externalizes numeric data to structured files so the model binds data programmatically rather than transcribing from the prompt. BIND improves GPT-5-Nano from 59.1% to 82.4% accuracy, matching pass@5 (82.0%) at lower token cost than pass@1, and GPT-5 from 86.2% to 95.8%. Furthermore, we validate our hypothesis by finetuning a model exclusively on binding and show that it outperforms end-to-end SFT and RL across three structurally distinct optimization categories, with a 1.5B binding specialist alone matching a 7B end-to-end baseline.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.21751 [cs.LG] |
| (or arXiv:2605.21751v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21751 arXiv-issued DOI via DataCite (pending registration) |
From: Zhiqi Gao [view email]
[v1]
Wed, 20 May 2026 21:25:41 UTC (201 KB)
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