diff --git a/exps/NATS-algos/search-size.py b/exps/NATS-algos/search-size.py
index 78727ee..f40a95f 100644
--- a/exps/NATS-algos/search-size.py
+++ b/exps/NATS-algos/search-size.py
@@ -1,28 +1,30 @@
 ##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
 ###########################################################################################################################################
+#
 # In this file, we aims to evaluate three kinds of channel searching strategies:
 # - channel-wise interpolation from "Network Pruning via Transformable Architecture Search, NeurIPS 2019"
-# - masking + Gumbel-Softmax from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
-# - masking + sampling from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
-# For simplicity, we use tas, fbv2, and tunas to refer these three strategies. Their official implementations are at the following links:
+# - masking + Gumbel-Softmax (mask_gumbel) from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020"
+# - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020"
+#
+# For simplicity, we use tas, mask_gumbel, and mask_rl to refer these three strategies. Their official implementations are at the following links:
 # - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NeurIPS-2019-TAS.md
 # - FBNetV2: https://github.com/facebookresearch/mobile-vision
 # - TuNAS: https://github.com/google-research/google-research/tree/master/tunas
 ####
-# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio 0.25
+# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --warmup_ratio 0.25
 ####
 # python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
 # python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
 # python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
 ####
-# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
-# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
-# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed 777
+# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
+# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
+# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --rand_seed 777
 ####
-# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 --use_api 0
-# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777
-# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed 777
+# python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777 --use_api 0
+# python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777
+# python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_rl --arch_weight_decay 0 --rand_seed 777
 ###########################################################################################################################################
 import os, sys, time, random, argparse
 import numpy as np
@@ -94,13 +96,13 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
     a_optimizer.zero_grad()
     _, logits, log_probs = network(arch_inputs)
     arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
-    if algo == 'tunas':
+    if algo == 'mask_rl':
       with torch.no_grad():
         RL_BASELINE_EMA.update(arch_prec1.item())
         rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
       rl_log_prob = sum(log_probs)
       arch_loss = - rl_advantage * rl_log_prob
-    elif algo == 'tas' or algo == 'fbv2':
+    elif algo == 'tas' or algo == 'mask_gumbel':
       arch_loss = criterion(logits, arch_targets)
     else:
       raise ValueError('invalid algorightm name: {:}'.format(algo))
@@ -231,7 +233,7 @@ def main(xargs):
 
     logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller))
 
-    if xargs.algo == 'fbv2' or xargs.algo == 'tas':
+    if xargs.algo == 'mask_gumbel' or xargs.algo == 'tas':
       network.set_tau(xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1))
       logger.log('[RESET tau as : {:}]'.format(network.tau))
     search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
@@ -291,7 +293,7 @@ if __name__ == '__main__':
   parser.add_argument('--data_path'   ,       type=str,   help='Path to dataset')
   parser.add_argument('--dataset'     ,       type=str,   choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
   parser.add_argument('--search_space',       type=str,   default='sss', choices=['sss'], help='The search space name.')
-  parser.add_argument('--algo'        ,       type=str,   choices=['tas', 'fbv2', 'tunas'], help='The search space name.')
+  parser.add_argument('--algo'        ,       type=str,   choices=['tas', 'mask_gumbel', 'mask_rl'], help='The search space name.')
   parser.add_argument('--genotype'    ,       type=str,   default='|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|', help='The genotype.')
   parser.add_argument('--use_api'     ,       type=int,   default=1, choices=[0,1], help='Whether use API or not (which will cost much memory).')
   # FOR GDAS