diff --git a/tests/test_sde.py b/tests/test_sde.py
index 955f9ab..048469b 100644
--- a/tests/test_sde.py
+++ b/tests/test_sde.py
@@ -53,8 +53,8 @@ def test_state_dependent_noise(model_class):
     eval_env = VecNormalize(DummyVecEnv([lambda: Monitor(gym.make(env_id))]), training=False, norm_reward=False)
     model = model_class('MlpPolicy', env, n_steps=200, max_grad_norm=1, use_rms_prop=False,
                         use_sde=True, ent_coef=0.00, verbose=1, create_eval_env=True, learning_rate=3e-4,
-                        policy_kwargs=dict(log_std_init=0.0, ortho_init=False, net_arch=[256, dict(pi=[256], vf=[256])]), seed=None)
-    # model.learn(total_timesteps=int(20000), log_interval=5, eval_freq=10000, eval_env=eval_env)
+                        policy_kwargs=dict(log_std_init=0.0, ortho_init=False, net_arch=[256, dict(pi=[256], vf=[256])]),
+                        seed=None)
     model.learn(total_timesteps=int(1000), log_interval=5, eval_freq=500, eval_env=eval_env)
 
 
@@ -62,4 +62,4 @@ def test_state_dependent_noise(model_class):
 def test_state_dependent_offpolicy_noise(model_class):
     model = model_class('MlpPolicy', 'Pendulum-v0', use_sde=True, seed=None, create_eval_env=True,
                         verbose=1, policy_kwargs=dict(log_std_init=-2))
-    model.learn(total_timesteps=int(20000), eval_freq=1000)
+    model.learn(total_timesteps=int(1000), eval_freq=500)