diff --git a/tests/test_run.py b/tests/test_run.py
index c2bc941..8d59976 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -14,7 +14,7 @@ def test_tqc(ent_coef):
         create_eval_env=True,
         ent_coef=ent_coef,
     )
-    model.learn(total_timesteps=500, eval_freq=250)
+    model.learn(total_timesteps=300, eval_freq=250)
 
 
 @pytest.mark.parametrize("n_critics", [1, 3])
@@ -23,7 +23,7 @@ def test_n_critics(n_critics):
     model = TQC(
         "MlpPolicy", "Pendulum-v0", policy_kwargs=dict(net_arch=[64], n_critics=n_critics), learning_starts=100, verbose=1
     )
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
 
 def test_sde():
@@ -35,6 +35,6 @@ def test_sde():
         learning_starts=100,
         verbose=1,
     )
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
     model.policy.reset_noise()
     model.policy.actor.get_std()
diff --git a/tests/test_save_load.py b/tests/test_save_load.py
index 9a73c77..396bc62 100644
--- a/tests/test_save_load.py
+++ b/tests/test_save_load.py
@@ -43,7 +43,7 @@ def test_save_load(tmp_path, model_class):
 
     # create model
     model = model_class("MlpPolicy", env, policy_kwargs=dict(net_arch=[16]), verbose=1)
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
     env.reset()
     observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)
@@ -152,7 +152,7 @@ def test_save_load(tmp_path, model_class):
         assert np.allclose(selected_actions, new_selected_actions, 1e-4)
 
         # check if learn still works
-        model.learn(total_timesteps=500)
+        model.learn(total_timesteps=300)
 
         del model
 
@@ -224,7 +224,7 @@ def test_save_load_replay_buffer(tmp_path, model_class):
     path = pathlib.Path(tmp_path / "logs/replay_buffer.pkl")
     path.parent.mkdir(exist_ok=True, parents=True)  # to not raise a warning
     model = model_class("MlpPolicy", select_env(model_class), buffer_size=1000)
-    model.learn(500)
+    model.learn(300)
     old_replay_buffer = deepcopy(model.replay_buffer)
     model.save_replay_buffer(path)
     model.replay_buffer = None
@@ -268,7 +268,7 @@ def test_save_load_policy(tmp_path, model_class, policy_str):
 
     # create model
     model = model_class(policy_str, env, policy_kwargs=dict(net_arch=[16]), verbose=1, **kwargs)
-    model.learn(total_timesteps=500)
+    model.learn(total_timesteps=300)
 
     env.reset()
     observations = np.concatenate([env.step([env.action_space.sample()])[0] for _ in range(10)], axis=0)