diff --git a/applications/ColossalMoE/train.py b/applications/ColossalMoE/train.py
index b2bdd4252..701a495c2 100644
--- a/applications/ColossalMoE/train.py
+++ b/applications/ColossalMoE/train.py
@@ -10,6 +10,7 @@ from torch.utils.data import Dataset
 from tqdm import tqdm
 from transformers import AutoTokenizer
 from transformers.models.mixtral import MixtralConfig, MixtralForCausalLM
+from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
 
 import colossalai
 from colossalai.booster import Booster
@@ -99,6 +100,10 @@ def parse_args():
     # optim
     parser.add_argument("--lr", type=float, default=1e-5, help="Learning rate.")
     parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
+    
+    # lr scheduler
+    parser.add_argument("--num_epochs", type=int, default=1, help="Number of training epochs")
+    parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
 
     # zero stage for all plugins
     parser.add_argument("--zero_stage", type=int, default=2, help="zero stage.")
@@ -203,10 +208,25 @@ def main():
 
     # Set optimizer
     optimizer = HybridAdam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+    
+    # Set lr scheduler
+    lr_scheduler = CosineAnnealingWarmupLR(
+        optimizer=optimizer,
+        total_steps=args.num_epochs * len(dataloader),
+        warmup_steps=args.warmup_steps
+        if args.warmup_steps is not None
+        else int(args.num_epochs * len(dataloader) * 0.025),
+        eta_min=0.1 * args.lr,
+    )
 
     # Set booster
     booster = Booster(plugin=plugin, **booster_kwargs)
-    model, optimizer, _, dataloader, _ = booster.boost(model=model, optimizer=optimizer, dataloader=dataloader)
+    model, optimizer, _, dataloader, lr_scheduler = booster.boost(
+        model=model,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        dataloader=dataloader,
+    )
     use_pipeline = isinstance(booster.plugin, MoeHybridParallelPlugin) and booster.plugin.pp_size > 1
     is_pp_last_stage = use_pipeline and booster.plugin.stage_manager.is_last_stage()
     pp_print_rank = is_pp_last_stage and (coordinator.local_rank == "0")
@@ -253,6 +273,7 @@ def main():
                     pbar.set_postfix({"loss": loss.item()})
 
                 optimizer.step()
+                lr_scheduler.step()
                 optimizer.zero_grad()
 
                 # Apply load balance