diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index ddd35b28..ebe5748c 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -32,9 +32,10 @@
 
 class Trainer(object):
     def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch_size=32, print_every=50,
-                 validate_every=-1, dev_data=None, save_path=None, optimizer=Adam(lr=0.01, weight_decay=0),
+                 validate_every=-1, dev_data=None, save_path=None, optimizer=Adam(lr=0.01, weight_decay=0), halve_lr_epochs=-1,
                  check_code_level=0, metric_key=None, sampler=RandomSampler(), prefetch=False, use_tqdm=True,
                  use_cuda=False, callbacks=None):
+
         """
         :param DataSet train_data: the training data
         :param torch.nn.modules.module model: a PyTorch model
@@ -47,6 +48,7 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch
         :param DataSet dev_data: the validation data
         :param str save_path: file path to save models
         :param Optimizer optimizer: an optimizer object
+        :param halve_lr_epochs: halve the learning rate if not imporving for [halve_lr_epochs] epochs. Default: -1 (never use it)
         :param int check_code_level: level of FastNLP code checker. -1: don't check, 0: ignore. 1: warning. 2: strict.\\
             `ignore` will not check unused field; `warning` when warn if some field are not used; `strict` means
             it will raise error if some field are not used. 检查的原理是通过使用很小的batch(默认两个sample)来检查代码是
@@ -108,6 +110,7 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch
         self.dev_data = dev_data  # If None, No validation.
         self.model = model
         self.losser = losser
+        self.halve_lr_epochs = halve_lr_epochs
         self.metrics = metrics
         self.n_epochs = int(n_epochs)
         self.batch_size = int(batch_size)
@@ -130,6 +133,9 @@ def __init__(self, train_data, model, loss=None, metrics=None, n_epochs=3, batch
 
         self.use_tqdm = use_tqdm
         self.print_every = abs(self.print_every)
+        
+        for group in self.optimizer.param_groups:
+            self.lr = group['lr']
 
         if self.dev_data is not None:
             self.tester = Tester(model=self.model,
@@ -227,6 +233,7 @@ def _train(self):
         else:
             inner_tqdm = tqdm
         self.step = 0
+        self.bad_valid = 0
         start = time.time()
         total_steps = (len(self.train_data) // self.batch_size + int(
             len(self.train_data) % self.batch_size != 0)) * self.n_epochs
@@ -302,6 +309,24 @@ def _do_validation(self, epoch, step):
             self.best_dev_perf = res
             self.best_dev_epoch = epoch
             self.best_dev_step = step
+        
+        # halve the learning rate if not improving for [halve_lr_epochs] epochs, and restart training from the best model.
+        else:
+            self.bad_valid += 1
+            if self.halve_lr_epochs != -1:
+                if self.bad_valid >= self.halve_lr_epochs:
+                    self.lr = self.lr / 2.0
+                    print("halve learning rate to {}".format(self.lr))
+                    model_name = "best_" + "_".join([self.model.__class__.__name__, self.metric_key, self.start_time])
+                    load_succeed = self._load_model(self.model, model_name)
+                    if load_succeed:
+                        print("Reloaded the best model.")
+                    else:
+                        print("Fail to reload best model.")
+                    self._set_lr(self.optimizer, self.lr)
+                    self.bad_valid = 0
+                    print("bad valid: {}".format(self.bad_valid))
+
         # get validation results; adjust optimizer
         self.callback_manager.on_valid_end(res, self.metric_key, self.optimizer)
         return res
@@ -406,7 +431,13 @@ def _better_eval_result(self, metrics):
                 else:
                     is_better = False
         return is_better
-
+    
+    def _set_lr(self, optimizer, lr):
+        # if self.optimizer == "YFOptimizer":
+        #     optimizer.set_lr_factor(lr)
+        # else:
+        for group in optimizer.param_groups:
+            group['lr'] = lr
 
 DEFAULT_CHECK_BATCH_SIZE = 2
 DEFAULT_CHECK_NUM_BATCH = 2