diff --git a/torchrl/objectives/crossq.py b/torchrl/objectives/crossq.py
index 2372b9e3163..b79c032ac9e 100644
--- a/torchrl/objectives/crossq.py
+++ b/torchrl/objectives/crossq.py
@@ -521,12 +521,15 @@ def actor_loss(
         log_prob = dist.log_prob(a_reparm)
 
         td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False)
+        self.qvalue_network.eval()
         td_q.set(self.tensor_keys.action, a_reparm)
         td_q = self._vmap_qnetworkN0(
             td_q,
             self._cached_detached_qvalue_params,
         )
+
         min_q = td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1)
+        self.qvalue_network.train()
 
         if log_prob.shape != min_q.shape:
             raise RuntimeError(
@@ -550,17 +553,6 @@ def qvalue_loss(
                 next_tensordict.set(self.tensor_keys.action, next_action)
                 next_sample_log_prob = next_dist.log_prob(next_action)
 
-        # TODO: separate forward pass seems faster than the combined.
-        # next_state_action_value = self._vmap_qnetworkN0(
-        #     next_tensordict.select(*self.qvalue_network.in_keys, strict=False),
-        #     self.qvalue_network_params,
-        # ).get(self.tensor_keys.state_action_value)
-
-        # current_state_action_value = self._vmap_qnetworkN0(
-        #     tensordict.select(*self.qvalue_network.in_keys, strict=False),
-        #     self.qvalue_network_params,
-        # ).get(self.tensor_keys.state_action_value)
-
         combined = torch.cat(
             [
                 tensordict.select(*self.qvalue_network.in_keys, strict=False),