diff --git a/src/training_scripts/autoregressive_quantiles.py b/src/training_scripts/autoregressive_quantiles.py
index c323c12..1fac750 100644
--- a/src/training_scripts/autoregressive_quantiles.py
+++ b/src/training_scripts/autoregressive_quantiles.py
@@ -15,7 +15,7 @@ from src.models.time_embedding_layer import TimeEmbedding
 
 #### ClearML ####
 clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
-task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Linear + Quarter + DoW + Load + Wind + Net")
+task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Non Linear")
 
 
 #### Data Processor ####
@@ -59,11 +59,11 @@ else:
         quantiles = eval(quantiles)
 
 model_parameters = {
-    "learning_rate": 0.0001,
+    "learning_rate": 0.001,
     "hidden_size": 512,
-    "num_layers": 2,
+    "num_layers": 4,
     "dropout": 0.2,
-    "time_feature_embedding": 4,
+    "time_feature_embedding": 8,
 }
 
 model_parameters = task.connect(model_parameters, name="model_parameters")
@@ -96,9 +96,9 @@ trainer = AutoRegressiveQuantileTrainer(
 trainer.add_metrics_to_track(
     [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
 )
-trainer.early_stopping(patience=10)
+trainer.early_stopping(patience=15)
 trainer.plot_every(5)
-trainer.train(task=task, epochs=epochs, remotely=False)
+trainer.train(task=task, epochs=epochs, remotely=True)
 
 ### Policy Evaluation ###
 idx_samples = trainer.test_set_samples