Accurate estimates of the Cloud Radiative Effect (CRE) require clear-sky radiative fluxes that are temporally consistent with the corresponding all-sky observations. Because passive satellite sensors cannot directly observe the sub-cloud clear-sky state, instantaneous clear-sky fluxes in cloudy regions are commonly approximated using radiative transfer calculations or temporally interpolated products, both of which introduce limitations. Here we present CSRFormer, a physically constrained deep-learning framework for estimating instantaneous clear-sky shortwave and longwave radiative fluxes over the global ocean. CSRFormer uses ERA5 atmospheric profiles, MERRA-2 aerosol properties, and NOAA sea surface temperature as inputs, and is trained against CERES Single Scanner Footprint (SSF) clear-sky observations. Over independent clear-sky samples, the model shows strong agreement with CERES SSF, with R&sup2; values of 0.81 for shortwave flux and 0.99 for longwave flux, corresponding to estimated uncertainties (RMSE) of ~3.2 W m⁻&sup2; and ~2.0 W m⁻&sup2;, respectively. Because the predictions are matched to the observation time of the CERES overpass, the resulting dataset avoids the temporal mismatch inherent in hourly mean products. Application of CSRFormer to cloudy scenes provides an observation-trained estimate of the theoretical clear-sky reference state beneath clouds for CRE analyses over the global ocean. The dataset is therefore a useful framework for examining the radiative effects of clouds and their covariation with cloud properties at the satellite overpass time. The CSRFormer v1.0 dataset is available at <a href="https://doi.org/10.5281/zenodo.20046058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20046058</a>.