diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 50f9d342..916aaa81 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -70,7 +70,7 @@ class GP(Model):
 
     def parameters_changed(self):
         self.posterior, self._log_marginal_likelihood, grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, Y_metadata=self.Y_metadata)
-        self.kern.update_gradients_full(grad_dict['dL_dK'])
+        self.kern.update_gradients_full(grad_dict['dL_dK'], self.X)
 
     def log_likelihood(self):
         return self._log_marginal_likelihood
diff --git a/GPy/kern/_src/rbf.py b/GPy/kern/_src/rbf.py
index 145bca5d..666a79f4 100644
--- a/GPy/kern/_src/rbf.py
+++ b/GPy/kern/_src/rbf.py
@@ -61,8 +61,8 @@ class RBF(Stationary):
 
         #from psi2
         S = variational_posterior.variance
-        denom, Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
-        d_length = 2.*psi2[:, :, :, None] * (Zdist_sq[None, :,:,:] * denom + mudist_sq + S[:, None, None, :] / l2) / (self.lengthscale * denom)
+        denom, _, Zdist_sq, _, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
+        d_length = 2.*psi2[:, :, :, None] * (Zdist_sq * denom + mudist_sq + S[:, None, None, :] / l2) / (self.lengthscale * denom)
         #TODO: combine denom and l2 as denom_l2??
         #TODO: tidy the above!
         #TODO: tensordot below?