Radiative transfer schemes in large-scale models tightly couple assumptions about cloud structure to methods for solving the radiative transfer equation, which makes these schemes inflexible, difficult to extend, and potentially susceptible to biases. A new technique, based on simultaneously sampling cloud state and spectral interval, provides radiative fluxes that are guaranteed to be unbiased with respect to the benchmark Independent Column Approximation and works equally well no matter how cloud structure is specified. Fluxes computed in this way are subject to random, uncorrelated errors that depend on the distribution of cloud optical properties. Seasonal forecasts, however, are not sensitive to this noise, making the method useful in weather and climate prediction models.