AI RESEARCH
Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies
arXiv CS.LG
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ArXi:2605.08558v1 Announce Type: new As an extension of the classical multi-armed bandit problem, multi-fidelity multi-armed bandits (MF-MAB) enable individual arms to be evaluated using diverse feedback sources that vary in both cost and accuracy. Prior stochastic models typically assume fixed low-to-high fidelity discrepancies, whereas modern proxy sources, such as learning-based simulators and Large Language Models (LLMs), can be improved using additional calibration.