We address the problem of simulating efficiently from the posterior distribution over the parameters of a particular class of nonlinear regression models using a Langevin–Metropolis sampler. It is shown that as the number of parameters increases, the proposal variance must scale in a precise way in order to converge to a diffusion. This generalizes previous results of Roberts and Rosenthal, showing the robustness of their analysis.