diff --git a/examples/advanced/2_calculate_validation_loss.py b/examples/advanced/2_calculate_validation_loss.py
index b312b7d0..c260c15d 100644
--- a/examples/advanced/2_calculate_validation_loss.py
+++ b/examples/advanced/2_calculate_validation_loss.py
@@ -15,7 +15,7 @@ from pathlib import Path
 import torch
 from huggingface_hub import snapshot_download
 
-from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
 device = torch.device("cuda")
@@ -42,26 +42,20 @@ delta_timestamps = {
 }
 
 # Load the last 10% of episodes of the dataset as a validation set.
-# - Load full dataset
-full_dataset = LeRobotDataset("lerobot/pusht", split="train")
-# - Calculate train and val subsets
-num_train_episodes = math.floor(full_dataset.num_episodes * 90 / 100)
-num_val_episodes = full_dataset.num_episodes - num_train_episodes
-print(f"Number of episodes in full dataset: {full_dataset.num_episodes}")
-print(f"Number of episodes in training dataset (90% subset): {num_train_episodes}")
-print(f"Number of episodes in validation dataset (10% subset): {num_val_episodes}")
-# - Get first frame index of the validation set
-first_val_frame_index = full_dataset.episode_data_index["from"][num_train_episodes].item()
-# - Load frames subset belonging to validation set using the `split` argument.
-#   It utilizes the `datasets` library's syntax for slicing datasets.
-#   For more information on the Slice API, please see:
-#   https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits
-train_dataset = LeRobotDataset(
-    "lerobot/pusht", split=f"train[:{first_val_frame_index}]", delta_timestamps=delta_timestamps
-)
-val_dataset = LeRobotDataset(
-    "lerobot/pusht", split=f"train[{first_val_frame_index}:]", delta_timestamps=delta_timestamps
-)
+# - Load dataset metadata
+dataset_metadata = LeRobotDatasetMetadata("lerobot/pusht")
+# - Calculate train and val episodes
+total_episodes = dataset_metadata.total_episodes
+episodes = list(range(dataset_metadata.total_episodes))
+num_train_episodes = math.floor(total_episodes * 90 / 100)
+train_episodes = episodes[:num_train_episodes]
+val_episodes = episodes[num_train_episodes:]
+print(f"Number of episodes in full dataset: {total_episodes}")
+print(f"Number of episodes in training dataset (90% subset): {len(train_episodes)}")
+print(f"Number of episodes in validation dataset (10% subset): {len(val_episodes)}")
+# - Load train an val datasets
+train_dataset = LeRobotDataset("lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps)
+val_dataset = LeRobotDataset("lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps)
 print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
 print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")