diff --git a/src/llmcompressor/datasets/utils.py b/src/llmcompressor/datasets/utils.py
index 269c45ca78..c49470025d 100644
--- a/src/llmcompressor/datasets/utils.py
+++ b/src/llmcompressor/datasets/utils.py
@@ -235,7 +235,9 @@ def _make_sampler(args: DatasetArguments, dataset: Dataset) -> Sampler:
 
         return RandomSampler(dataset, num_samples=num_samples)
     else:
-        return LengthAwareSampler(dataset, num_samples=num_samples)
+        return LengthAwareSampler(
+            dataset, num_samples=num_samples, batch_size=batch_size
+        )
 
 
 def data_collator_with_truncation(
@@ -269,9 +271,11 @@ def __init__(
         self,
         data_source: Dataset,
         num_samples: Optional[int] = None,
+        batch_size: int = 1,
     ) -> None:
         self.data_source = data_source
         self._num_samples = num_samples or len(data_source)
+        self.batch_size = batch_size
 
         if "input_ids" in data_source.column_names:
             feature_name = "input_ids"
@@ -284,6 +288,40 @@ def __init__(
 
         lengths = [len(sample) for sample in data_source[feature_name]]
         self.order = torch.argsort(torch.tensor(lengths), descending=True).tolist()
+        self._calculate_and_log_batch_stats(lengths)
+
+    def _calculate_and_log_batch_stats(self, lengths: list[int]):
+        if self.batch_size == 1:
+            return
+
+        logger.debug(
+            "LengthAwareSampler: Calculating batch statistics for "
+            f"{self.num_samples} samples with batch size {self.batch_size}"
+        )
+
+        sorted_lengths = [lengths[i] for i in self.order][: self.num_samples]
+        total_tokens_removed = 0
+        total_tokens_added = 0
+
+        for i in range(0, self.num_samples, self.batch_size):
+            batch_lengths = sorted_lengths[i : i + self.batch_size]
+            if not batch_lengths:
+                continue
+
+            shortest_in_batch = min(batch_lengths)
+            longest_in_batch = max(batch_lengths)
+            tokens_removed = sum(lgth - shortest_in_batch for lgth in batch_lengths)
+            tokens_added = sum(longest_in_batch - lgth for lgth in batch_lengths)
+
+            total_tokens_removed += tokens_removed
+            total_tokens_added += tokens_added
+
+        if total_tokens_removed > 0 or total_tokens_added > 0:
+            logger.debug(
+                f"LengthAwareSampler: Total token overhead - "
+                f"removed (truncation): {total_tokens_removed}, "
+                f"added (padding): {total_tokens_added}"
+            )
 
     @property
     def num_samples(self) -> int:
diff --git a/tests/llmcompressor/datasets/__init__.py b/tests/llmcompressor/datasets/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/llmcompressor/datasets/test_length_aware_sampler.py b/tests/llmcompressor/datasets/test_length_aware_sampler.py
new file mode 100644
index 0000000000..3f19d18d99
--- /dev/null
+++ b/tests/llmcompressor/datasets/test_length_aware_sampler.py
@@ -0,0 +1,42 @@
+from unittest.mock import patch
+
+import pytest
+from datasets import Dataset
+
+from llmcompressor.datasets.utils import LengthAwareSampler
+
+
+def _create_mock_dataset(lengths: list[int]) -> Dataset:
+    """Create a mock dataset with input_ids of specified lengths."""
+    return Dataset.from_dict({"input_ids": [[0] * length for length in lengths]})
+
+
+class TestLengthAwareSampler:
+    """Tests for LengthAwareSampler batch statistics logging."""
+
+    @pytest.mark.unit
+    def test_batch_size_parameter(self):
+        dataset = _create_mock_dataset([100, 200, 300])
+        sampler = LengthAwareSampler(dataset, batch_size=4)
+        assert sampler.batch_size == 4
+
+    @pytest.mark.unit
+    def test_logging_called_when_batch_size_greater_than_one(self):
+        dataset = _create_mock_dataset([100, 150, 200, 250])
+
+        with patch("llmcompressor.datasets.utils.logger") as mock_logger:
+            LengthAwareSampler(dataset, batch_size=2)
+            debug_calls = [str(c) for c in mock_logger.debug.call_args_list]
+            assert any("Calculating batch statistics" in c for c in debug_calls)
+
+    @pytest.mark.unit
+    def test_tokens_added_calculation(self):
+        dataset = _create_mock_dataset([100, 200, 300, 150])
+
+        with patch("llmcompressor.datasets.utils.logger") as mock_logger:
+            LengthAwareSampler(dataset, batch_size=2)
+
+            debug_calls = [str(c) for c in mock_logger.debug.call_args_list]
+            assert any(
+                "added (padding): 150" in c for c in debug_calls
+            ), f"Expected 'added (padding): 150' in {debug_calls}"