diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
index 9ce4f69da..d94a2f725 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
@@ -153,8 +153,8 @@ def get_params() -> AttributeDict:
 def load_checkpoint_if_available(
     params: AttributeDict,
     model: nn.Module,
-    optimizer: torch.optim.Optimizer,
-    scheduler: torch.optim.lr_scheduler._LRScheduler = None,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
 ) -> None:
     """Load checkpoint from file.
 
@@ -198,6 +198,8 @@ def load_checkpoint_if_available(
     for k in keys:
         params[k] = saved_params[k]
 
+    return saved_params
+
 
 def save_checkpoint(
     params: AttributeDict,
@@ -485,6 +487,9 @@ def run(rank, world_size, args):
         num_classes=max_phone_id + 1,  # +1 for the blank symbol
         subsampling_factor=params.subsampling_factor,
     )
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
     model.to(device)
     if world_size > 1:
         model = DDP(model, device_ids=[rank])
@@ -496,9 +501,8 @@ def run(rank, world_size, args):
     )
     scheduler = StepLR(optimizer, step_size=8, gamma=0.1)
 
-    load_checkpoint_if_available(
-        params=params, model=model, optimizer=optimizer
-    )
+    optimizer.load_state_dict(checkpoints["optimizer"])
+    scheduler.load_state_dict(checkpoints["scheduler"])
 
     librispeech = LibriSpeechAsrDataModule(args)
     train_dl = librispeech.train_dataloaders()
diff --git a/icefall/checkpoint.py b/icefall/checkpoint.py
index 3dc1d9436..e45df4fe4 100644
--- a/icefall/checkpoint.py
+++ b/icefall/checkpoint.py
@@ -94,7 +94,7 @@ def load_checkpoint(
         s = checkpoint[name]
         if obj and s:
             obj.load_state_dict(s)
-        checkpoint.pop(name)
+            checkpoint.pop(name)
 
     load("optimizer", optimizer)
     load("scheduler", scheduler)