diff --git a/src/instructlab/training/main_ds.py b/src/instructlab/training/main_ds.py
index 7ea01c64..807c16d3 100644
--- a/src/instructlab/training/main_ds.py
+++ b/src/instructlab/training/main_ds.py
@@ -346,9 +346,12 @@ def train(args, model, tokenizer, train_loader, grad_accum, metric_logger):
 
     for epoch in range(args.num_epochs):
         torch.distributed.barrier()
-        torch.distributed.breakpoint()
-        if hasattr(train_loader.batch_sampler, 'set_epoch'):
+        if args.sampler in ('multipack'):
             train_loader.batch_sampler.set_epoch(epoch)
+        elif args.sampler in ('distributed'):
+            train_loader.sampler.set_epoch(epoch)
+        else:
+            raise NotADirectoryError
 
         if local_rank == 0:
             inner_pb = tqdm(range(len(train_loader)), desc=f"Epoch {epoch}")
@@ -512,7 +515,8 @@ def main(args):
         is_granite=args.is_granite,
         max_batch_len=args.max_batch_len,
         packing_max_batch_len=packing_max_batch_len,
-        batch_sampler=args.batch_sampler,
+        samples_per_gpu=args.samples_per_gpu,
+        sampler=args.sampler,
         seed=args.seed,
     )
 
@@ -678,13 +682,13 @@ def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs):
         ],
     )
     parser.add_argument(
-        "--batch_sampler",
+        "--sampler",
         type=str,
         default="multipack",
         help="The batch sampler type to use.",
         choices=[
             "multipack",
-            "default"
+            "distributed"
         ],
     )
     parser.add_argument("--num_warmup_steps", type=int, default=1000)
diff --git a/src/instructlab/training/token_dataset.py b/src/instructlab/training/token_dataset.py
index 1bbfe3f6..cab2f169 100644
--- a/src/instructlab/training/token_dataset.py
+++ b/src/instructlab/training/token_dataset.py
@@ -85,7 +85,8 @@ def setup_dataloader(
     is_granite=False,
     max_batch_len=60000,
     packing_max_batch_len=60000,
-    batch_sampler='multipack',
+    samples_per_gpu=None,
+    sampler='multipack',
     seed=47,
 ) -> DataLoader:
     collate_fn = make_collate_fn(
@@ -95,7 +96,7 @@ def setup_dataloader(
     world_size = int(os.environ["WORLD_SIZE"])
 
     lengths = dataset.get_lengths()
-    if batch_sampler == 'multipack':
+    if sampler == 'multipack':
         sampler = MultipackDistributedBatchSampler(
             batch_max_length=packing_max_batch_len,
             lengths=lengths,
@@ -104,12 +105,23 @@ def setup_dataloader(
             seed=seed,
             padding=not is_granite,
         )
-    elif batch_sampler == 'default':
-        sampler = None
+        sampler = {'batch_sampler': sampler}
+    elif sampler == 'distributed':
+        from torch.utils.data import DistributedSampler
+        sampler = (
+            DistributedSampler(dataset) if 
+            torch.distributed.is_initialized() else None
+        )
+        sampler = {
+            'sampler': sampler,
+            'batch_size': samples_per_gpu,
+        }
+    else:
+        raise NotImplementedError
 
     dataloader = DataLoader(
         dataset,
-        batch_sampler=sampler,
+        **sampler,
         num_workers=num_workers,
         collate_fn=collate_fn,
     )