diff --git a/pearl/utils/scripts/cb_benchmark/cb_benchmark_config.py b/pearl/utils/scripts/cb_benchmark/cb_benchmark_config.py
index f822005a..cb8d10ed 100644
--- a/pearl/utils/scripts/cb_benchmark/cb_benchmark_config.py
+++ b/pearl/utils/scripts/cb_benchmark/cb_benchmark_config.py
@@ -31,8 +31,10 @@
 from pearl.utils.instantiations.environments.contextual_bandit_uci_environment import (
     SLCBEnvironment,
 )
-
-DATA_PATH: str = "./utils/instantiations/environments/uci_datasets"
+if os.path.exists("../Pearl"):
+    DATA_PATH: str = "pearl/utils/instantiations/environments/uci_datasets"
+else:
+    DATA_PATH: str = "./utils/instantiations/environments/uci_datasets"
 
 """
 Experiment config
diff --git a/pearl/utils/scripts/cb_benchmark/run_cb_benchmarks.py b/pearl/utils/scripts/cb_benchmark/run_cb_benchmarks.py
index 5b326423..0049e427 100644
--- a/pearl/utils/scripts/cb_benchmark/run_cb_benchmarks.py
+++ b/pearl/utils/scripts/cb_benchmark/run_cb_benchmarks.py
@@ -275,6 +275,16 @@ def run_cb_benchmarks(
 
     # Create UCI data directory if it does not already exist
     uci_data_path = "./utils/instantiations/environments/uci_datasets"
+    print("current_dir", os.getcwd())
+    print("dirs list", os.listdir())
+    
+    if os.path.exists("../Pearl"):
+        uci_data_path = "pearl/utils/instantiations/environments/uci_datasets"
+        save_results_path: str = "pearl/utils/scripts/cb_benchmark/experiments_results"
+        print("dir exists", os.path.exists("pearl/utils/instantiations/environments/"))
+    else:
+        uci_data_path = "utils/instantiations/environments/uci_datasets"
+        save_results_path: str = "utils/scripts/cb_benchmark/experiments_results"
     if not os.path.exists(uci_data_path):
         os.makedirs(uci_data_path)
 
@@ -282,7 +292,7 @@ def run_cb_benchmarks(
     download_uci_data(data_path=uci_data_path)
 
     # Create folder for result if it does not already exist
-    save_results_path: str = "./utils/scripts/cb_benchmark/experiments_results"
+    
     if not os.path.exists(save_results_path):
         os.makedirs(save_results_path)
 
diff --git a/test/unit/test_tutorials/test_rec_system.py b/test/unit/test_tutorials/test_rec_system.py
index 65b5ad84..4f66efdf 100644
--- a/test/unit/test_tutorials/test_rec_system.py
+++ b/test/unit/test_tutorials/test_rec_system.py
@@ -6,6 +6,7 @@
 import random
 import unittest
 from typing import List, Optional, Tuple
+import os
 
 import numpy as np
 
@@ -66,7 +67,7 @@
 This environment's underlying model was pre-trained using the MIND dataset (Wu et al. 2020).
 The model is defined by class `SequenceClassificationModel` below.
 The model's state dict is saved in
-tutorials/single_item_recommender_system_example/env_model_state_dict.pt
+tutorials/single_item_recommender_system_example/env_model_state_dict
 
 Each data point is:
 - A history of impressions clicked by a user
@@ -189,16 +190,26 @@ def setUp(self) -> None:
     def test_rec_system(self) -> None:
         # load environment
         model = SequenceClassificationModel(100).to(device)
+        print("current dir", os.getcwd())
+        print("dirs list", os.listdir())
+        if os.path.exists("../Pearl"):
+            # Github CI tests
+            print("Pearl directory exists")
+            model_dir = "tutorials/single_item_recommender_system_example/"
+        else:
+            # Meta internal tests
+            print("pearl directory exists")
+            model_dir = "pearl/tutorials/single_item_recommender_system_example/"
         model.load_state_dict(
             # Note: in the tutorial the directory "pearl" must be replaced by "Pearl"
             torch.load(
-                "pearl/tutorials/single_item_recommender_system_example/env_model_state_dict.pt",
+                os.path.join(model_dir, "env_model_state_dict.pt"),
                 weights_only=True,
             )
         )
         # Note: in the tutorial the directory "pearl" must be replaced by "Pearl"
         actions = torch.load(
-            "pearl/tutorials/single_item_recommender_system_example/news_embedding_small.pt",
+            os.path.join(model_dir, "news_embedding_small.pt"),
             weights_only=True,
         )
         history_length = 8