diff --git a/metastable_baselines/distributions/distributions.py b/metastable_baselines/distributions/distributions.py
index 879bad2..90534cd 100644
--- a/metastable_baselines/distributions/distributions.py
+++ b/metastable_baselines/distributions/distributions.py
@@ -79,13 +79,16 @@ def get_legal_setups(allowedEPTs=None, allowedParStrength=None, allowedCovStreng
         for cs in allowedCovStrength:
             if ps.value > cs.value:
                 continue
-            for ept in allowedEPTs:
-                if cs == Strength.FULL:
-                    for pt in allowedPTs:
-                        if pt != ParametrizationType.NONE:
-                            yield (ps, cs, ept, pt)
-                else:
-                    yield (ps, cs, ept, ParametrizationType.NONE)
+            if cs == Strength.NONE:
+                yield (ps, cs, EnforcePositiveType.NONE, ParametrizationType.NONE)
+            else:
+                for ept in allowedEPTs:
+                    if cs == Strength.FULL:
+                        for pt in allowedPTs:
+                            if pt != ParametrizationType.NONE:
+                                yield (ps, cs, ept, pt)
+                    else:
+                        yield (ps, cs, ept, ParametrizationType.NONE)
 
 
 def make_proba_distribution(
@@ -147,6 +150,10 @@ class UniversalGaussianDistribution(SB3_Distribution):
         assert (parameterization_type != ParametrizationType.NONE) == (
             cov_strength == Strength.FULL), 'You should set an ParameterizationType iff the cov-strength is full'
 
+        if self.par_type == ParametrizationType.SPHERICAL_CHOL and self.enforce_positive_type == EnforcePositiveType.NONE:
+            raise Exception(
+                'You need to specify an enforce_positive_type for spherical_cholesky')
+
     def new_dist_like_me(self, mean: th.Tensor, chol: th.Tensor):
         p = self.distribution
         if isinstance(p, Independent):
@@ -223,9 +230,9 @@ class UniversalGaussianDistribution(SB3_Distribution):
     def mode(self) -> th.Tensor:
         return self.distribution.mean
 
-    def actions_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor, deterministic: bool = False) -> th.Tensor:
+    def actions_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor, deterministic: bool = False, latent_pi=None) -> th.Tensor:
         # Update the proba distribution
-        self.proba_distribution(mean_actions, log_std)
+        self.proba_distribution(mean_actions, log_std, latent_pi=latent_pi)
         return self.get_actions(deterministic=deterministic)
 
     def log_prob_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor) -> Tuple[th.Tensor, th.Tensor]: