Generalized posteriors update a prior through an exponential transformation of empirical loss rather than a conventional likelihood, with a learning rate controlling posterior concentration. Their credible intervals may have incorrect frequentist coverage because posterior dispersion need not reflect sampling variability. We propose infinitesimal-jackknife variance matching (IJVM), a computationally efficient method for selecting the learning rate for a prespecified scalar functional. IJVM matches the functional's posterior variance to an infinitesimal-jackknife estimate of the sampling variance of its posterior mean, computed from posterior covariances with observation-level losses. A multiplicative one-step update requires one pilot posterior fit and a second fit for inference, avoiding explicit estimation of loss Hessians or gradient covariance matrices and repeated bootstrap posterior fitting. Under suitable regularity and Monte Carlo accuracy conditions, we establish consistency of the selected rate for the functional-specific first-order calibration target. With additional sampling and posterior-quantile assumptions, the resulting credible intervals achieve asymptotically nominal frequentist coverage. Simulations involving homoscedastic and heteroscedastic regression and hinge-loss classification show that one-step IJVM achieves coverage near the nominal 95% level, with interval lengths comparable to bootstrap-based generalized posterior calibration but substantially fewer posterior fits. Additional updates offer little improvement.

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