diff --git a/test/dataloader2/test_mprs.py b/test/dataloader2/test_mprs.py
index 05a7ba27b..b4fb1d7f8 100644
--- a/test/dataloader2/test_mprs.py
+++ b/test/dataloader2/test_mprs.py
@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+
 import multiprocessing as mp
 import unittest
 from unittest import TestCase
@@ -13,7 +14,7 @@
 from torch.utils.data.datapipes.iter.sharding import SHARDING_PRIORITIES
 
 from torchdata.dataloader2 import DataLoader2, DataLoader2Iterator, MultiProcessingReadingService
-from torchdata.datapipes.iter import IterableWrapper, IterDataPipe
+from torchdata.datapipes.iter import IterableWrapper
 
 
 def _add_one(x: int) -> int:
@@ -45,17 +46,6 @@ def _dispatching_dp(n_elements=1000):
     return dp
 
 
-class NonShardableDataPipe(IterDataPipe):
-    def __init__(self, dp: IterDataPipe):
-        self.dp = dp
-
-    def is_replicable(self):
-        return False
-
-    def __iter__(self):
-        yield from self.dp
-
-
 class TestMultiProcessingReadingService(TestCase):
     r"""
     This tests specific functionalities of MultiProcessingReadingService, notably
@@ -74,7 +64,7 @@ def test_early_exit(self, ctx, dp_fn, main_prefetch, worker_prefetch) -> None:
             worker_prefetch_cnt=worker_prefetch,
             multiprocessing_context=ctx,
         )
-        dl: DataLoader2 = DataLoader2(dp, reading_service=rs)
+        dl = DataLoader2(dp, reading_service=rs)
         it = iter(dl)
         for _ in range(10):
             _ = next(it)
@@ -92,7 +82,7 @@ def test_exit(self, ctx, dp_fn, main_prefetch, worker_prefetch) -> None:
             worker_prefetch_cnt=worker_prefetch,
             multiprocessing_context=ctx,
         )
-        dl: DataLoader2 = DataLoader2(dp, reading_service=rs)
+        dl = DataLoader2(dp, reading_service=rs)
         _ = list(dl)
         dl.shutdown()
 
@@ -258,54 +248,6 @@ def test_reading_service_limit(self, dp, n_workers, worker_prefetch_cnt, main_pr
             res.append(x)
         self.assertEqual(9, len(res))
 
-    def test_initial_epoch_checkpointing(self):
-        dp = IterableWrapper(range(20)).shuffle().sharding_filter()
-        # Note that the second `shuffle` occurs in the main process, which uses a different RNG from
-        # the `shuffle` done in the worker processes
-        dp = NonShardableDataPipe(dp).shuffle()  # type: ignore[assignment, arg-type]
-        rs = MultiProcessingReadingService(num_workers=2)
-
-        # Functional Test: Saving state before iterator is created
-        dl: DataLoader2 = DataLoader2(datapipe=dp, reading_service=rs)
-        dl.seed(1)
-        initial_state = dl.state_dict()
-        it1 = iter(dl)
-
-        restored_dl: DataLoader2 = DataLoader2.from_state(initial_state, rs)  # type: ignore[arg-type]
-        restored_dl._restore_checkpoint_beginning_of_epoch()
-        self.assertEqual(list(it1), list(restored_dl))
-
-        dl.shutdown()
-        restored_dl.shutdown()
-
-        # Functional Test: Saving state after iterator is created
-        dl = DataLoader2(datapipe=dp, reading_service=rs)
-        dl.seed(1)
-        it1 = iter(dl)
-        initial_state = dl.state_dict()
-
-        restored_dl = DataLoader2.from_state(initial_state, rs)  # type: ignore[arg-type]
-        restored_dl._restore_checkpoint_beginning_of_epoch()
-        self.assertEqual(list(it1), list(restored_dl))
-
-        dl.shutdown()
-        restored_dl.shutdown()
-
-        # Functional Test: Saving state after iterator is created and began iterating
-        dl = DataLoader2(datapipe=dp, reading_service=rs)
-        dl.seed(1)
-        it1 = iter(dl)
-        temp = next(it1)  # Starts iterating
-        initial_state = dl.state_dict()
-
-        restored_dl = DataLoader2.from_state(initial_state, rs)  # type: ignore[arg-type]
-        restored_dl._restore_checkpoint_beginning_of_epoch()
-
-        self.assertEqual([temp] + list(it1), list(restored_dl))  # Note skipping over 1st element from actual result
-
-        dl.shutdown()
-        restored_dl.shutdown()
-
     # TODO: Test cases when there is official support of `pause` and `resume` with round-robin sharding
     #       Currently, using sharding_round_robin raises a warning
     # def test_round_robin_dispatching_pause_limit(self):
diff --git a/torchdata/dataloader2/random/seed_generator.py b/torchdata/dataloader2/random/seed_generator.py
index fa4fdfabc..2f67ee2be 100644
--- a/torchdata/dataloader2/random/seed_generator.py
+++ b/torchdata/dataloader2/random/seed_generator.py
@@ -83,13 +83,3 @@ def spawn(self, worker_id: int, inplace: bool = False) -> "SeedGenerator":
             self._worker_rng = self._worker_rng.spawn(worker_id)
             return self
         return SeedGenerator(seed=None, _rngs=(self._shared_rng.clone(), self._worker_rng.spawn(worker_id)))
-
-    def __getstate__(self):
-        state = (
-            self._shared_rng,
-            self._worker_rng,
-        )
-        return state
-
-    def __setstate__(self, state):
-        self._shared_rng, self._worker_rng = state
diff --git a/torchdata/dataloader2/reading_service.py b/torchdata/dataloader2/reading_service.py
index d8a43e349..4109c05fe 100644
--- a/torchdata/dataloader2/reading_service.py
+++ b/torchdata/dataloader2/reading_service.py
@@ -312,8 +312,6 @@ def initialize_iteration(
     ) -> Optional[Callable[[DataPipe], DataPipe]]:
         assert self._end_datapipe is not None
 
-        # Set random seeds for DataPipe that are in the main process (NOT those in worker processes)
-        # Worker seeds are set in `process_reset_fn`
         set_graph_random_seed(self._end_datapipe, seed_generator)
 
         if self._mp: