When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values. Yet LLM-assisted data curation creates a new operational tradeoff: higher-fidelity models and richer prompts can be substantially more expensive, while the value of this additional fidelity depends on the downstream optimization accuracy. This raises the central question of this work: when should one pay for a better LLM, given a specific downstream task at hand? We study this question through stochastic optimization with mixed-fidelity first-order oracles, which can have systematic bias and state-dependent variance. We develop two cost-aware a...
| Authors | Swati Gupta, Tianjiao Li |
|---|---|
| Primary category | math.OC |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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