diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py
index ed385bc8..03f5d478 100644
--- a/GPy/inference/latent_function_inference/var_dtc_parallel.py
+++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py
@@ -94,8 +94,12 @@ class VarDTC_minibatch(LatentFunctionInference):
         
         for n_start in xrange(0,num_data,self.batchsize):            
             n_end = min(self.batchsize+n_start, num_data)
-            Y_slice = Y[n_start:n_end]
-            X_slice = X[n_start:n_end]
+            if (n_end-n_start)==num_data:
+                Y_slice = Y
+                X_slice = X
+            else:
+                Y_slice = Y[n_start:n_end]
+                X_slice = X[n_start:n_end]
             
             if het_noise:
                 b = beta[n_start]
@@ -347,7 +351,9 @@ def update_gradients(model, mpi_comm=None):
     while not isEnd:
         isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, X, model.Z, model.likelihood, Y)
         if isinstance(model.X, VariationalPosterior):
-            if mpi_comm ==None:
+            if (n_range[1]-n_range[0])==X.shape[0]:
+                X_slice = X
+            elif mpi_comm ==None:
                 X_slice = model.X[n_range[0]:n_range[1]]
             else:
                 X_slice = model.X[model.N_range[0]+n_range[0]:model.N_range[0]+n_range[1]]
diff --git a/GPy/kern/_src/add.py b/GPy/kern/_src/add.py
index d63c9ab8..cda80209 100644
--- a/GPy/kern/_src/add.py
+++ b/GPy/kern/_src/add.py
@@ -63,13 +63,16 @@ class Add(CombinationKernel):
         target = np.zeros(X.shape)
         [target.__iadd__(p.gradients_X_diag(dL_dKdiag, X)) for p in self.parts]
         return target
-
+    
+    @Cache_this(limit=2, force_kwargs=['which_parts'])
     def psi0(self, Z, variational_posterior):
         return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
-
+    
+    @Cache_this(limit=2, force_kwargs=['which_parts'])
     def psi1(self, Z, variational_posterior):
         return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))
 
+    @Cache_this(limit=2, force_kwargs=['which_parts'])
     def psi2(self, Z, variational_posterior):
         psi2 = reduce(np.add, (p.psi2(Z, variational_posterior) for p in self.parts))
         #return psi2
diff --git a/GPy/kern/_src/psi_comp/__init__.py b/GPy/kern/_src/psi_comp/__init__.py
index 38094394..1395ad98 100644
--- a/GPy/kern/_src/psi_comp/__init__.py
+++ b/GPy/kern/_src/psi_comp/__init__.py
@@ -10,7 +10,7 @@ import sslinear_psi_comp
 
 class PSICOMP_RBF(Pickleable):
 
-    @Cache_this(limit=1, ignore_args=(0,))
+    @Cache_this(limit=2, ignore_args=(0,))
     def psicomputations(self, variance, lengthscale, Z, variational_posterior):
         if isinstance(variational_posterior, variational.NormalPosterior):
             return rbf_psi_comp.psicomputations(variance, lengthscale, Z, variational_posterior)
@@ -19,7 +19,7 @@ class PSICOMP_RBF(Pickleable):
         else:
             raise ValueError, "unknown distriubtion received for psi-statistics"
                 
-    @Cache_this(limit=1, ignore_args=(0,1,2,3))
+    @Cache_this(limit=2, ignore_args=(0,1,2,3))
     def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
         if isinstance(variational_posterior, variational.NormalPosterior):
             return rbf_psi_comp.psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior)
@@ -30,7 +30,7 @@ class PSICOMP_RBF(Pickleable):
         
 class PSICOMP_Linear(Pickleable):
 
-    @Cache_this(limit=1, ignore_args=(0,))
+    @Cache_this(limit=2, ignore_args=(0,))
     def psicomputations(self, variance, Z, variational_posterior):
         if isinstance(variational_posterior, variational.NormalPosterior):
             raise NotImplementedError
@@ -39,7 +39,7 @@ class PSICOMP_Linear(Pickleable):
         else:
             raise ValueError, "unknown distriubtion received for psi-statistics"
                 
-    @Cache_this(limit=1, ignore_args=(0,1,2,3))
+    @Cache_this(limit=2, ignore_args=(0,1,2,3))
     def psiDerivativecomputations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, Z, variational_posterior):
         if isinstance(variational_posterior, variational.NormalPosterior):
             raise NotImplementedError