diff --git a/rsl_rl/modules/normalizer.py b/rsl_rl/modules/normalizer.py
index 771efcf..c80ad10 100644
--- a/rsl_rl/modules/normalizer.py
+++ b/rsl_rl/modules/normalizer.py
@@ -26,7 +26,7 @@ def __init__(self, shape, eps=1e-2, until=None):
         self.register_buffer("_mean", torch.zeros(shape).unsqueeze(0))
         self.register_buffer("_var", torch.ones(shape).unsqueeze(0))
         self.register_buffer("_std", torch.ones(shape).unsqueeze(0))
-        self.count = 0
+        self.register_buffer("count", torch.tensor(0, dtype=torch.long))
 
     @property
     def mean(self):
diff --git a/rsl_rl/runners/on_policy_runner.py b/rsl_rl/runners/on_policy_runner.py
index 5f2458a..b475620 100644
--- a/rsl_rl/runners/on_policy_runner.py
+++ b/rsl_rl/runners/on_policy_runner.py
@@ -42,12 +42,8 @@ def __init__(self, env: VecEnv, train_cfg, log_dir=None, device="cpu"):
         self.save_interval = self.cfg["save_interval"]
         self.empirical_normalization = self.cfg["empirical_normalization"]
         if self.empirical_normalization:
-            if train_cfg.get("resume") == True:
-                until = 0
-            else:
-                until = 1.0e8
-            self.obs_normalizer = EmpiricalNormalization(shape=[num_obs], until=until).to(self.device)
-            self.critic_obs_normalizer = EmpiricalNormalization(shape=[num_critic_obs], until=until).to(self.device)
+            self.obs_normalizer = EmpiricalNormalization(shape=[num_obs], until=1.0e8).to(self.device)
+            self.critic_obs_normalizer = EmpiricalNormalization(shape=[num_critic_obs], until=1.0e8).to(self.device)
         else:
             self.obs_normalizer = torch.nn.Identity()  # no normalization
             self.critic_obs_normalizer = torch.nn.Identity()  # no normalization
@@ -264,6 +260,7 @@ def save(self, path, infos=None):
         if self.empirical_normalization:
             saved_dict["obs_norm_state_dict"] = self.obs_normalizer.state_dict()
             saved_dict["critic_obs_norm_state_dict"] = self.critic_obs_normalizer.state_dict()
+
         torch.save(saved_dict, path)
 
         # Upload model to external logging service