diff --git a/ss2r/algorithms/mbpo/losses.py b/ss2r/algorithms/mbpo/losses.py
index de95f5ade..99b586e1c 100644
--- a/ss2r/algorithms/mbpo/losses.py
+++ b/ss2r/algorithms/mbpo/losses.py
@@ -83,7 +83,8 @@ def critic_loss(
         action = transitions.action
         scale = cost_scaling if safe else reward_scaling
         gamma = safety_discounting if safe else discounting
-        q_old_action = qr_network.apply(
+        q_net = qc_network if (safe and qc_network is not None) else qr_network
+        q_old_action = q_net.apply(
             normalizer_params, q_params, transitions.observation, action
         )
         key, another_key = jax.random.split(key)
@@ -101,7 +102,7 @@ def policy(obs: jax.Array) -> tuple[jax.Array, jax.Array]:
             next_action = parametric_action_distribution.postprocess(next_action)
             return next_action, next_log_prob
 
-        q_fn = lambda obs, action: qr_network.apply(
+        q_fn = lambda obs, action: q_net.apply(
             normalizer_params, target_q_params, obs, action
         )
         target_q = target_q_fn(
diff --git a/ss2r/algorithms/mbpo/model_env.py b/ss2r/algorithms/mbpo/model_env.py
index 7dd689229..5834dfb15 100644
--- a/ss2r/algorithms/mbpo/model_env.py
+++ b/ss2r/algorithms/mbpo/model_env.py
@@ -112,9 +112,11 @@ def step(self, state: base.State, action: jax.Array) -> base.State:
                 ).mean(axis=-1)
                 backup_policy = self.policy_network.apply
                 backup_policy_params = self.backup_policy_params
-                backup_action = backup_policy(
-                    self.initial_normalizer_params, backup_policy_params, state.obs
-                )[: self.action_size]
+                backup_action = jnp.tanh(
+                    backup_policy(
+                        self.initial_normalizer_params, backup_policy_params, state.obs
+                    )[: self.action_size]
+                )
                 qc_backup = self.qc_network.apply(
                     self.normalizer_params,
                     self.backup_qc_params,
@@ -130,9 +132,11 @@ def step(self, state: base.State, action: jax.Array) -> base.State:
 
             pred_backup_action = self.policy_network.apply
             backup_policy_params = self.backup_policy_params
-            backup_action = pred_backup_action(
-                self.normalizer_params, backup_policy_params, state.obs
-            )[: self.action_size]
+            backup_action = jnp.tanh(
+                pred_backup_action(
+                    self.normalizer_params, backup_policy_params, state.obs
+                )[: self.action_size]
+            )
             pred_qr = self.qr_network.apply
             backup_qr_params = self.backup_qr_params
             pessimistic_qr_pred = pred_qr(
@@ -226,13 +230,13 @@ def _propagate_ensemble(
     """Propagate the ensemble predictions based on the selection method."""
     # Calculate the nominal predictions
     if ensemble_selection == "nominal":
-        # Get the average model parameters
-        avg_model_params = jax.tree_util.tree_map(
-            lambda p: jnp.mean(p, axis=0), model_params
-        )
-        next_obs, reward, cost = pred_fn(
-            normalizer_params, avg_model_params, obs, action
+        vmap_pred_fn = jax.vmap(pred_fn, in_axes=(None, 0, None, None))
+        next_obs_pred, reward_pred, cost_pred = vmap_pred_fn(
+            normalizer_params, model_params, obs, action
         )
+        next_obs = jax.tree_map(lambda x: jnp.mean(x, axis=0), next_obs_pred)
+        reward = jnp.mean(reward_pred, axis=0)
+        cost = jnp.mean(cost_pred, axis=0)
     elif ensemble_selection == "random":
         vmap_pred_fn = jax.vmap(pred_fn, in_axes=(None, 0, None, None))
         next_obs_pred, reward_pred, cost_pred = vmap_pred_fn(
@@ -243,14 +247,6 @@ def _propagate_ensemble(
         next_obs = jax.tree_map(lambda x: x[idx], next_obs_pred)
         reward = reward_pred[idx]
         cost = cost_pred[idx]
-    elif ensemble_selection == "mean":
-        vmap_pred_fn = jax.vmap(pred_fn, in_axes=(None, 0, None, None))
-        next_obs_pred, reward_pred, cost_pred = vmap_pred_fn(
-            normalizer_params, model_params, obs, action
-        )
-        next_obs = jax.tree_map(lambda x: jnp.mean(x, axis=0), next_obs_pred)
-        reward = jnp.mean(reward_pred, axis=0)
-        cost = jnp.mean(cost_pred, axis=0)
     else:
         raise ValueError(f"Unknown ensemble selection: {ensemble_selection}")
     return next_obs, reward, cost
diff --git a/ss2r/algorithms/mbpo/on_policy_training_step.py b/ss2r/algorithms/mbpo/on_policy_training_step.py
index 5bd20ddfc..c90d552a1 100644
--- a/ss2r/algorithms/mbpo/on_policy_training_step.py
+++ b/ss2r/algorithms/mbpo/on_policy_training_step.py
@@ -303,9 +303,11 @@ def relabel_transitions(
 
         pred_backup_action = planning_env.policy_network.apply
         backup_policy_params = planning_env.backup_policy_params
-        backup_action = pred_backup_action(
-            normalizer_params, backup_policy_params, transitions.observation
-        )[..., : planning_env.action_size]
+        backup_action = jnp.tanh(
+            pred_backup_action(
+                normalizer_params, backup_policy_params, transitions.observation
+            )[..., : planning_env.action_size]
+        )
         disagreement = (
             next_obs_pred.std(axis=0).mean(-1)
             if isinstance(next_obs_pred, jax.Array)