diff --git a/rsl_rl/algorithms/ppo.py b/rsl_rl/algorithms/ppo.py
index 0e788972..71e7b3de 100644
--- a/rsl_rl/algorithms/ppo.py
+++ b/rsl_rl/algorithms/ppo.py
@@ -513,7 +513,7 @@ def broadcast_parameters(self) -> None:
         self.actor.load_state_dict(model_params[0])
         self.critic.load_state_dict(model_params[1])
         if self.rnd:
-            self.rnd.predictor.load_state_dict(model_params[1])
+            self.rnd.predictor.load_state_dict(model_params[2])
 
     def reduce_parameters(self) -> None:
         """Collect gradients from all GPUs and average them.
diff --git a/rsl_rl/models/cnn_model.py b/rsl_rl/models/cnn_model.py
index f79cf468..034cf46b 100644
--- a/rsl_rl/models/cnn_model.py
+++ b/rsl_rl/models/cnn_model.py
@@ -119,9 +119,9 @@ def get_latent(
         latent_1d = super().get_latent(obs)
         # Process 2D observation groups with CNNs
         latent_cnn_list = [self.cnns[obs_group](obs[obs_group]) for obs_group in self.obs_groups_2d]
-        latend_cnn = torch.cat(latent_cnn_list, dim=-1)
+        latent_cnn = torch.cat(latent_cnn_list, dim=-1)
         # Concatenate 1D and CNN latents
-        return torch.cat([latent_1d, latend_cnn], dim=-1)
+        return torch.cat([latent_1d, latent_cnn], dim=-1)
 
     def as_jit(self) -> nn.Module:
         """Return a version of the model compatible with Torch JIT export."""
diff --git a/rsl_rl/modules/rnn.py b/rsl_rl/modules/rnn.py
index 2cdf7158..fe4116a0 100644
--- a/rsl_rl/modules/rnn.py
+++ b/rsl_rl/modules/rnn.py
@@ -62,7 +62,7 @@ def reset(self, dones: torch.Tensor | None = None, hidden_state: HiddenState = N
                 else:
                     self.hidden_state[..., dones == 1, :] = 0.0
             else:
-                NotImplementedError(
+                raise NotImplementedError(
                     "Resetting the hidden state of done environments with a custom hidden state is not implemented"
                 )