Adaptive Sampling

Adaptive sampling studies how to choose which examples, measurements, or computational steps to spend a limited budget on. Common approaches use uncertainty, prediction error, information gain, or difficulty to guide active learning, Bayesian experimental design, bandit allocation, and adaptive numerical solvers; learned policies, including reinforcement learning, are also used. Applications range from scientific experiments and sensor acquisition to diffusion-model sampling and test-time reasoning with language models. A central challenge is ensuring that the signals used to prioritize samples reliably improve the final task without sacrificing coverage, introducing bias, or costing more to compute than they save.