diff --git a/test/nodes/test_mds_sampler.py b/test/nodes/test_mds_sampler.py
new file mode 100644
index 000000000..5ef79d059
--- /dev/null
+++ b/test/nodes/test_mds_sampler.py
@@ -0,0 +1,39 @@
+import itertools
+
+from parameterized import parameterized
+from torch.testing._internal.common_utils import TestCase
+from torchdata.nodes.adapters import IterableWrapper
+from torchdata.nodes.prefetch import Prefetcher
+
+from torchdata.nodes.samplers.multi_node_weighted_sampler import MultiNodeWeightedSampler
+from torchdata.nodes.samplers.stop_criteria import StopCriteria
+
+from utils import DummyIterableDataset, run_test_save_load_state
+
+
+num_samples = 5
+num_datasets = 4
+
+datasets = {
+    f"ds{i}": IterableWrapper(DummyIterableDataset(num_samples, f"ds{i}"))
+    for i in range(num_datasets)
+}
+
+weights_fn = lambda i: 0.1 * (i + 1)
+weights = {f"ds{i}": weights_fn(i) for i in range(num_datasets)}
+mixer = MultiNodeWeightedSampler(datasets, weights, StopCriteria.FIRST_DATASET_EXHAUSTED, seed=42)
+
+num_epochs = 1
+
+for epoch in range(num_epochs):
+    results = list(mixer)
+    
+    datasets_in_results = [result["name"] for result in results]
+
+    dataset_counts_in_results = [datasets_in_results.count(f"ds{i}") for i in range(num_datasets)]
+    elements = [[result["name"], result["step"]] for result in results]
+    print(elements)
+    print(datasets_in_results)
+    print(dataset_counts_in_results)
+
+    mixer.reset()
diff --git a/test/nodes/test_multi_node_weighted_sampler.py b/test/nodes/test_multi_node_weighted_sampler.py
index b9f00d926..fa02eb272 100644
--- a/test/nodes/test_multi_node_weighted_sampler.py
+++ b/test/nodes/test_multi_node_weighted_sampler.py
@@ -88,12 +88,7 @@ def test_multi_node_weighted_batch_sampler_zero_weights(
 
     def test_multi_node_weighted_sampler_first_exhausted(self) -> None:
         """Test MultiNodeWeightedSampler with stop criteria FIRST_DATASET_EXHAUSTED"""
-        mixer = self._setup_multi_node_weighted_sampler(
-            self._num_samples,
-            self._num_datasets,
-            self._weights_fn,
-            stop_criteria=StopCriteria.FIRST_DATASET_EXHAUSTED,
-        )
+        mixer = MultiNodeWeightedSampler(self.datasets, self.weights, StopCriteria.FIRST_DATASET_EXHAUSTED, seed=42)
 
         for _ in range(self._num_epochs):
             results = list(mixer)
diff --git a/torchdata/nodes/samplers/multi_node_weighted_sampler.py b/torchdata/nodes/samplers/multi_node_weighted_sampler.py
index 572b57b26..fd39bf5fb 100644
--- a/torchdata/nodes/samplers/multi_node_weighted_sampler.py
+++ b/torchdata/nodes/samplers/multi_node_weighted_sampler.py
@@ -158,17 +158,21 @@ def _check_for_stop_iteration(self) -> None:
         # the first dataset is exhausted. Doing this to correctly catch StopIteration
         # when trying next(it) on already exhausted iterator
         if self.stop_criteria == StopCriteria.FIRST_DATASET_EXHAUSTED and any(self._datasets_exhausted.values()):
+            print(self._datasets_exhausted)
+            print("Stopping: First dataset exhausted.")
             raise StopIteration()
 
         return
 
     def next(self) -> T:
         self._started = True
+        
         while True:
             self._check_for_stop_iteration()
 
             # Fetch the next item's key from the weighted sampler
             key = next(self._weighted_sampler)
+            print(f"The next key selected is {key}")
             try:
                 if self._datasets_exhausted[key] and self.stop_criteria == StopCriteria.ALL_DATASETS_EXHAUSTED:
                     # Before fetching a new item check if key corresponds to an already
@@ -190,6 +194,7 @@ def next(self) -> T:
                 # reset the iterator and try again
                 self.source_nodes[key].reset()
                 item = next(self.source_nodes[key])
+            print(self._datasets_exhausted)
             break
 
         # If we did't throw StopIteration, increment the number of items yielded and return the item