diff --git a/torch/optim/lr_scheduler.py b/torch/optim/lr_scheduler.py
index 42f194b0f40..8de3af4b87f 100644
--- a/torch/optim/lr_scheduler.py
+++ b/torch/optim/lr_scheduler.py
@@ -5,6 +5,7 @@ import weakref
 from bisect import bisect_right
 from collections import Counter
 from functools import partial, wraps
+from typing import Optional, Sequence
 
 from torch import inf
 
@@ -781,18 +782,29 @@ class SequentialLR(LRScheduler):
     def __init__(
         self, optimizer, schedulers, milestones, last_epoch=-1, verbose="deprecated"
     ):
-        for scheduler_idx in range(len(schedulers)):
-            if schedulers[scheduler_idx].optimizer != optimizer:
+        if len(schedulers) < 1:
+            raise ValueError(
+                f"{self.__class__.__name__} expects at least one scheduler, but got no scheduler."
+            )
+
+        for scheduler_idx, scheduler in enumerate(schedulers):
+            if not hasattr(scheduler, "optimizer"):
+                raise TypeError(
+                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
+                )
+            if isinstance(scheduler, ReduceLROnPlateau):
                 raise ValueError(
-                    "Sequential Schedulers expects all schedulers to belong to the same optimizer, but "
-                    f"got schedulers at index {scheduler_idx} to be different than the optimizer passed in."
+                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
+                    "requires additional kwargs to be specified when calling `step`, "
+                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
+                )
+            if optimizer != scheduler.optimizer:
+                raise ValueError(
+                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
+                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
+                    f"which is different from {optimizer.__class__.__name__}."
                 )
 
-            if schedulers[scheduler_idx].optimizer != schedulers[0].optimizer:
-                raise ValueError(
-                    "Sequential Schedulers expects all schedulers to belong to the same optimizer, but "
-                    f"got schedulers at index {0} and {scheduler_idx} to be different."
-                )
         if len(milestones) != len(schedulers) - 1:
             raise ValueError(
                 "Sequential Schedulers expects number of schedulers provided to be one more "
@@ -1024,12 +1036,13 @@ class CosineAnnealingLR(LRScheduler):
 
 
 class ChainedScheduler(LRScheduler):
-    """Chains list of learning rate schedulers. It takes a list of chainable learning
+    """Chains list of learning rate schedulers. It takes a sequence of chainable learning
     rate schedulers and performs consecutive step() functions belonging to them by just
     one call.
 
     Args:
-        schedulers (list): List of chained schedulers.
+        schedulers (sequence): sequence of chained schedulers.
+        optimizer (Optimizer, optional): Wrapped optimizer. Default: None.
 
     Example:
         >>> # xdoctest: +SKIP
@@ -1041,22 +1054,41 @@ class ChainedScheduler(LRScheduler):
         >>> # lr = 0.59049  if epoch >= 4
         >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
         >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
-        >>> scheduler = ChainedScheduler([scheduler1, scheduler2])
+        >>> scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer)
         >>> for epoch in range(100):
         >>>     train(...)
         >>>     validate(...)
         >>>     scheduler.step()
     """
 
-    def __init__(self, schedulers):
-        for scheduler_idx in range(1, len(schedulers)):
-            if schedulers[scheduler_idx].optimizer != schedulers[0].optimizer:
-                raise ValueError(
-                    "ChainedScheduler expects all schedulers to belong to the same optimizer, but "
-                    f"got schedulers at index {0} and {scheduler_idx} to be different"
+    def __init__(
+        self, schedulers: Sequence[LRScheduler], optimizer: Optional[Optimizer] = None
+    ):
+        if len(schedulers) < 1:
+            raise ValueError(
+                f"{self.__class__.__name__} expects at least one scheduler to be chained, but got no scheduler."
+            )
+
+        optimizer = optimizer or schedulers[0].optimizer
+        for scheduler_idx, scheduler in enumerate(schedulers):
+            if not hasattr(scheduler, "optimizer"):
+                raise TypeError(
+                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
                 )
-        self._schedulers = list(schedulers)
-        self.optimizer = schedulers[0].optimizer
+            if isinstance(scheduler, ReduceLROnPlateau):
+                raise ValueError(
+                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
+                    "requires additional kwargs to be specified when calling `step`, "
+                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
+                )
+            if optimizer != scheduler.optimizer:
+                raise ValueError(
+                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
+                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
+                    f"which is different from {optimizer.__class__.__name__}."
+                )
+        self._schedulers = schedulers
+        self.optimizer = optimizer
         self._last_lr = [
             group["lr"] for group in self._schedulers[-1].optimizer.param_groups
         ]
diff --git a/torch/optim/lr_scheduler.pyi b/torch/optim/lr_scheduler.pyi
index 2446c80bc54..ce3f26a4d21 100644
--- a/torch/optim/lr_scheduler.pyi
+++ b/torch/optim/lr_scheduler.pyi
@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, Iterable, List, Optional, Union
+from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Union
 
 from .optimizer import Optimizer
 
@@ -109,7 +109,11 @@ class ExponentialLR(LRScheduler):
     ) -> None: ...
 
 class ChainedScheduler(LRScheduler):
-    def __init__(self, schedulers: List[LRScheduler]) -> None: ...
+    def __init__(
+        self,
+        schedulers: Sequence[LRScheduler],
+        optimizer: Optional[Optimizer] = ...,
+    ) -> None: ...
 
 class SequentialLR(LRScheduler):
     def __init__(