diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
index f35571a0356..efb48cb1c6c 100755
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -2098,6 +2098,7 @@ def prepare_data_loader(
             even_batches=self.even_batches,
             slice_fn_for_dispatch=slice_fn_for_dispatch,
             use_seedable_sampler=self.use_seedable_sampler,
+            data_seed=self.dataloader_config.data_seed,
             non_blocking=self.non_blocking,
             use_stateful_dataloader=self.use_stateful_dataloader,
         )
diff --git a/src/accelerate/data_loader.py b/src/accelerate/data_loader.py
index c793e85f8dc..bf3f35fb7e8 100644
--- a/src/accelerate/data_loader.py
+++ b/src/accelerate/data_loader.py
@@ -77,9 +77,11 @@ class SeedableRandomSampler(RandomSampler):
     """
 
     def __init__(self, *args, **kwargs):
+        data_seed = kwargs.pop("data_seed", None)
         super().__init__(*args, **kwargs)
+
+        self.initial_seed = data_seed if data_seed is not None else torch.random.initial_seed()
         self.epoch = 0
-        self.initial_seed = torch.random.initial_seed()
 
     def __iter__(self):
         if self.generator is None:
@@ -937,6 +939,7 @@ def prepare_data_loader(
     even_batches: bool = True,
     slice_fn_for_dispatch: Optional[Callable] = None,
     use_seedable_sampler: bool = False,
+    data_seed: Optional[int] = None,
     non_blocking: bool = False,
     use_stateful_dataloader: bool = False,
 ) -> DataLoader:
@@ -996,6 +999,9 @@ def prepare_data_loader(
             reproducability. Comes at a cost of potentially different performances due to different shuffling
             algorithms but ensures results will be the *exact* same. Should be paired with `set_seed()` at every
             `self.set_epoch`
+        data_seed (`int`, *optional*, defaults to `None`):
+            The seed to use for the underlying generator when using `use_seedable_sampler`. If `None`, the generator
+            will use the current default seed from torch.
         non_blocking (`bool`, *optional*, defaults to `False`):
             If set to `True`, dataloader will utilize non-blocking host-to-device transfers. If the dataloader has
             `pin_memory` set to `True`, this will help to increase overlap between data transfer and computations.
@@ -1069,6 +1075,7 @@ def prepare_data_loader(
             replacement=sampler.replacement,
             num_samples=sampler._num_samples,
             generator=getattr(sampler, "generator", torch.Generator()),
+            data_seed=data_seed,
         )
 
     if isinstance(dataloader.sampler, RandomSampler) and state.distributed_type == DistributedType.XLA:
diff --git a/src/accelerate/test_utils/scripts/test_script.py b/src/accelerate/test_utils/scripts/test_script.py
index 1d54d098a63..3b6f302f76a 100644
--- a/src/accelerate/test_utils/scripts/test_script.py
+++ b/src/accelerate/test_utils/scripts/test_script.py
@@ -400,6 +400,34 @@ def check_seedable_sampler_in_batch_sampler_shard():
     ), "Sampler in BatchSamplerShard is not SeedableRandomSampler."
 
 
+def check_seedable_sampler_with_data_seed():
+    # Set seed
+    set_seed(42)
+    data_seed = 42
+    train_set = RegressionDataset(length=10, seed=42)
+    train_dl = DataLoader(train_set, batch_size=2, shuffle=True)
+
+    config = DataLoaderConfiguration(use_seedable_sampler=True, data_seed=data_seed)
+    accelerator = Accelerator(dataloader_config=config)
+    prepared_dl = accelerator.prepare(train_dl)
+    original_items = []
+    for _ in range(3):
+        for batch in prepared_dl:
+            original_items.append(batch["x"])
+    original_items = torch.cat(original_items)
+
+    # Set new data seed
+    config.data_seed = 43
+    accelerator = Accelerator(dataloader_config=config)
+    prepared_dl = accelerator.prepare(train_dl)
+    new_items = []
+    for _ in range(3):
+        for batch in prepared_dl:
+            new_items.append(batch["x"])
+    new_items = torch.cat(new_items)
+    assert not torch.allclose(original_items, new_items), "Obtained the same items with different data seed."
+
+
 def mock_training(length, batch_size, generator, use_seedable_sampler=False):
     set_seed(42)
     generator.manual_seed(42)
@@ -800,6 +828,7 @@ def main():
         central_dl_preparation_check()
         custom_sampler_check()
         check_seedable_sampler()
+        check_seedable_sampler_with_data_seed()
 
     if state.num_processes > 1:
         check_seedable_sampler_in_batch_sampler_shard()
diff --git a/src/accelerate/utils/dataclasses.py b/src/accelerate/utils/dataclasses.py
index a06ceb0f659..a28cc52ebdf 100644
--- a/src/accelerate/utils/dataclasses.py
+++ b/src/accelerate/utils/dataclasses.py
@@ -737,6 +737,9 @@ class DataLoaderConfiguration:
             training results are fully reproducable using a different sampling technique. While seed-to-seed results
             may differ, on average the differences are neglible when using multiple different seeds to compare. Should
             also be ran with [`~utils.set_seed`] for the best results.
+        data_seed (`int`, defaults to `None`):
+            The seed to use for the underlying generator when using `use_seedable_sampler`. If `None`, the generator
+            will use the current default seed from torch.
         non_blocking (`bool`, defaults to `False`):
             If set to `True`, the dataloader prepared by the Accelerator will utilize non-blocking host-to-device
             transfers, allowing for better overlap between dataloader communication and computation. Recommended that
@@ -781,6 +784,13 @@ class DataLoaderConfiguration:
             "multiple different seeds to compare. Should also be ran with [`~utils.set_seed`] for the best results."
         },
     )
+    data_seed: int = field(
+        default=None,
+        metadata={
+            "help": "The seed to use for the underlying generator when using `use_seedable_sampler`. If `None`, the generator"
+            " will use the current default seed from torch."
+        },
+    )
     non_blocking: bool = field(
         default=False,
         metadata={