diff --git a/paddlepalm/multihead_trainer.py b/paddlepalm/multihead_trainer.py
index 39ec9711cc3c88286213461d72bc3b91329cd9ed..bc7af3448eaff179e7c48b329a783acdf6555ad3 100644
--- a/paddlepalm/multihead_trainer.py
+++ b/paddlepalm/multihead_trainer.py
@@ -1,11 +1,10 @@
 
 from paddle import fluid
 from paddle.fluid import layers
-from paddlepalm.distribute import gpu_dev_count, cpu_dev_count
+from paddlepalm.distribute import gpu_dev_count, cpu_dev_count, data_feeder, decode_fake
 from paddlepalm import Trainer
 from paddlepalm.utils import reader_helper
 import numpy as np
-from paddlepalm.distribute import gpu_dev_count, data_feeder, decode_fake
 import time
 
 dev_count = 1 if gpu_dev_count <= 1 else gpu_dev_count
@@ -61,10 +60,6 @@ class MultiHeadTrainer(Trainer):
         """
         Build forward computation graph for training, which usually built from input layer to loss node.
 
-        Args:
-            backbone: a Backbone object with phase == 'train', which is used to extract multi-level text features, e.g., contextual word embedding and sentence embedding.
-            heads: a list of Head objects. Phase of each head should be set as 'train', which is used to build task specific output layers.
-        
         Return:
             - loss_var: a Variable object. The computational graph variable(node) of loss.
         """
@@ -115,10 +110,14 @@ class MultiHeadTrainer(Trainer):
 
     def fit_readers_with_mixratio(self, readers, sampling_reference, num_epochs, phase='train'):
         """
-        Bind readers and loaded train/predict data to trainers. 
+        Bind readers and loaded train/predict data to trainers. The `num_epochs` argument only 
+            works on `sampling_reference` task(trainer), and num_epochs of other tasks are infered from 
+            their `mix_ratio`.
 
         Args:
             readers: a dict or list of Reader objects. For dict case, each key is a trainer's name, and the mapped value is the reader object to bind to the trainer. For list case, each 
+            sampling_reference: a trainer name. The task(trainer) selected as baseline for task sampling. 
+            num_epochs: training epochs of the sampling_reference task (trainer). 
         """
         self._check_phase(phase)