diff --git a/src/instructlab/sdg/sdg.py b/src/instructlab/sdg/sdg.py
index ef0b9ef6..30b57ccd 100644
--- a/src/instructlab/sdg/sdg.py
+++ b/src/instructlab/sdg/sdg.py
@@ -6,13 +6,14 @@
 import uuid
 
 # Third Party
-from datasets import Dataset, concatenate_datasets, load_dataset
+from datasets import Dataset, load_dataset
 from datasets.data_files import EmptyDatasetError
 from tqdm import tqdm
 
 # Local
 from .logger_config import setup_logger
 from .pipeline import Pipeline
+from .utils.datautils import safe_concatenate_datasets
 
 
 logger = setup_logger(__name__)
@@ -81,6 +82,7 @@ def _generate_data(pipelines, input_split, i=None):
 
     def generate(self, dataset: Dataset, checkpoint_dir=None) -> Dataset:
         # check if checkpoint_dir exists
+        pre_generated_data = []
         if checkpoint_dir is not None:
             try:
                 # check if there are any existing checkpoints
@@ -91,10 +93,13 @@ def generate(self, dataset: Dataset, checkpoint_dir=None) -> Dataset:
                     f"Loading existing checkpoints from {checkpoint_dir}, with {pre_generated_data.num_rows} rows"
                 )
                 seed_data = self._get_missing_data(dataset, pre_generated_data)
-                logger.info(
-                    f"Found {seed_data.num_rows} missing rows in the dataset"   
-                )
-            
+                if seed_data.num_rows == 0:
+                    logger.info(
+                        f"All seed data has been generated, no missing rows found, returning data from {checkpoint_dir}"
+                    )
+                    return pre_generated_data
+                logger.info(f"Found {seed_data.num_rows} missing rows in the dataset")
+
             except EmptyDatasetError:
                 logger.info(
                     f"No existing checkpoints found in {checkpoint_dir}, generating from scratch"
@@ -122,9 +127,9 @@ def generate(self, dataset: Dataset, checkpoint_dir=None) -> Dataset:
             f"Generating dataset with {len(input_splits)} splits, batch size {self.batch_size}, and {self.num_workers} workers"
         )
 
-        generated_data = []
+        generated_data = [pre_generated_data] if pre_generated_data else []
         last_saved_split_index = 0  # To track the last saved split
-        
+
         with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
             futures = [
                 executor.submit(self._generate_data, self.pipelines, input_split, i)
@@ -140,13 +145,13 @@ def generate(self, dataset: Dataset, checkpoint_dir=None) -> Dataset:
                     if self.save_freq and (i + 1) % self.save_freq == 0:
                         # Save only the new splits since the last checkpoint
                         new_splits = generated_data[last_saved_split_index : i + 1]
-                        checkpoint_dataset = concatenate_datasets(new_splits)
+                        checkpoint_dataset = safe_concatenate_datasets(new_splits)
                         self._save_intermediate_checkpoint(
                             checkpoint_dataset, checkpoint_dir
                         )
 
                         last_saved_split_index = i + 1
 
-        generated_dataset = concatenate_datasets(generated_data)
+        generated_dataset = safe_concatenate_datasets(generated_data)
 
         return generated_dataset