Bandits in Prod: Hyperparameter Optimization at Inference Time
Many production systems can assess a configuration only by using it on live requests and observing noisy feedback. Modern agentic systems are a prominent example, with inference-time choices such as model selection, retrieval depth, prompting strategy, and decoding temperature, yet often with no representative validation data. We formalize this setting as Online Hyperparameter Optimization (OHPO) and cast it as an infinitely many-armed bandit over mixed and conditional search spaces. We introduce IMABO, a general framework that combines any bandit policy for choosing among already sampled configurations with any oracle for proposing new ones. We instantiate it with IMOSS, a restart-free a...
| Authors | Louis Abraham, Tuan-Anh Nguyen, Nicolas Devatine |
|---|---|
| Primary category | cs.LG |
| Published | 1 September 2026 |
| Last updated at source | 2 September 2026 |
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