The proper choice of the θ value 5 has been tackled by different workarounds. As a result, various methods to image reconstruction, ensemble refinement, and related problems can be distinguished by their choice of a solution parameterized by a multiplicative parameter θ of the KL divergence: solutions are chosen by L-curve analysis; 7-10 by using algorithms to determine an elbow of the L-curve; so that the resulting error is the most-likely according to some statistic; 1 by putting a prior on θ and integrating it out; by selecting a perfect fit ignoring errors completely as in classical MaxEnt methods; by not including θ as parameter at all, which corresponds to setting θ = 1; or by cross-validation. While these workarounds are useful in practice, they generally do not balance information from experiment and theory properly in the sense of using a priori knowledge. As a result, the solutions obtained tend to be overfitted or underfitted. Moreover, a solution is not always guaranteed to exist for some of these workarounds. Here, we propose how to choose the θ parameter a priori for gentle ensemble refinement. Often, ensemble refinement is quite crude, such that the optimal ensemble deviates drastically from the reference ensemble. In gentle refinement, we take care that this is not case. In this regime, the expected KL divergence SKL can be approximated in terms of the mean-squared error of the potential energy surface U defining the force field used to create the reference ensemble, SKL ≈ var(β∆U )/2. Here, energies are measured in units of the thermal energy, β = 1/(k B T ), in the equilibrium Boltzmann distributions of reference and refined ensembles, with k B the Boltzmann constant and T the absolute temperature. We propose to use this physically meaningful relation to encode prior knowledge about the expected force-field error by setting