diff --git a/CHANGE-LOG.md b/CHANGE-LOG.md
index 2a6247e..40fbc3b 100644
--- a/CHANGE-LOG.md
+++ b/CHANGE-LOG.md
@@ -6,3 +6,4 @@
 - [2019.01.31] [13e908f] GDAS codes were publicly released.
 - [2020.07.01] [a45808b] Upgrade NAS-API to the 2.0 version.
 - [2020.09.16] [7052265] Create NATS-BENCH.
+- [2020.10.15] [       ] Update NATS-BENCH to version 1.0
diff --git a/docs/NATS-Bench.md b/docs/NATS-Bench.md
index 48ebc81..f266a72 100644
--- a/docs/NATS-Bench.md
+++ b/docs/NATS-Bench.md
@@ -7,6 +7,7 @@ We analyze the validity of our benchmark in terms of various criteria and perfor
 We also show the versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NAS algorithms on it. All logs and diagnostic information trained using the same setup for each candidate are provided.
 This facilitates a much larger community of researchers to focus on developing better NAS algorithms in a more comparable and computationally effective environment.
 
+**You can use `pip install nats_bench` to install the library of NATS-Bench.**
 
 The structure of this Markdown file:
 - [How to use NATS-Bench?](#How-to-Use-NATS-Bench)
@@ -175,18 +176,18 @@ python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HO
 python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
 
 
-Run the channel search strategy in FBNet-V2
+Run the channel search strategy in FBNet-V2 -- masking + Gumbel-Softmax :
 
-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
 
 
-Run the channel search strategy in TuNAS:
+Run the channel search strategy in TuNAS -- masking + sampling :
 
-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
 ```
 
 ### Final Discovered Architectures for Each Algorithm
@@ -250,7 +251,7 @@ GDAS:
 If you find that NATS-Bench helps your research, please consider citing it:
 ```
 @article{dong2020nats,
-  title={NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size},
+  title={{NATS-Bench}: Benchmarking NAS algorithms for Architecture Topology and Size},
   author={Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
   journal={arXiv preprint arXiv:2009.00437},
   year={2020}
diff --git a/exps/NATS-algos/search-size.py b/exps/NATS-algos/search-size.py
index f40a95f..e215523 100644
--- a/exps/NATS-algos/search-size.py
+++ b/exps/NATS-algos/search-size.py
@@ -43,7 +43,7 @@ from models       import get_cell_based_tiny_net, get_search_spaces
 from nats_bench   import create
 
 
-# Ad-hoc for TuNAS
+# Ad-hoc for RL algorithms.
 class ExponentialMovingAverage(object):
   """Class that maintains an exponential moving average."""
 
diff --git a/exps/experimental/vis-nats-bench-ws.py b/exps/experimental/vis-nats-bench-ws.py
index 8b01ebe..de4a22a 100644
--- a/exps/experimental/vis-nats-bench-ws.py
+++ b/exps/experimental/vis-nats-bench-ws.py
@@ -44,8 +44,8 @@ def fetch_data(root_dir='./output/search', search_space='tss', dataset=None, suf
     # alg2name['FBNetV2'] = 'fbv2-affine0_BN0{:}'.format(suffix)
     # alg2name['TuNAS'] = 'tunas-affine0_BN0{:}'.format(suffix)
     alg2name['channel-wise interpolation'] = 'tas-affine0_BN0-AWD0.001{:}'.format(suffix)
-    alg2name['masking + Gumbel-Softmax'] = 'fbv2-affine0_BN0-AWD0.001{:}'.format(suffix)
-    alg2name['masking + sampling'] = 'tunas-affine0_BN0-AWD0.0{:}'.format(suffix)
+    alg2name['masking + Gumbel-Softmax'] = 'mask_gumbel-affine0_BN0-AWD0.001{:}'.format(suffix)
+    alg2name['masking + sampling'] = 'mask_rl-affine0_BN0-AWD0.0{:}'.format(suffix)
   for alg, name in alg2name.items():
     alg2path[alg] = os.path.join(ss_dir, dataset, name, 'seed-{:}-last-info.pth')
   alg2data = OrderedDict()
diff --git a/lib/models/shape_searchs/generic_size_tiny_cell_model.py b/lib/models/shape_searchs/generic_size_tiny_cell_model.py
index 9a3f6d0..ee887cc 100644
--- a/lib/models/shape_searchs/generic_size_tiny_cell_model.py
+++ b/lib/models/shape_searchs/generic_size_tiny_cell_model.py
@@ -3,8 +3,8 @@
 #####################################################
 # Here, we utilized three techniques to search for the number of channels:
 # - 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"
+# - 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"
 from typing import List, Text, Any
 import random, torch
 import torch.nn as nn
@@ -52,10 +52,10 @@ class GenericNAS301Model(nn.Module):
   def set_algo(self, algo: Text):
     # used for searching
     assert self._algo is None, 'This functioin can only be called once.'
-    assert algo in ['fbv2', 'tunas', 'tas'], 'invalid algo : {:}'.format(algo)
+    assert algo in ['mask_gumbel', 'mask_rl', 'tas'], 'invalid algo : {:}'.format(algo)
     self._algo = algo
     self._arch_parameters = nn.Parameter(1e-3*torch.randn(self._max_num_Cs, len(self._candidate_Cs)))
-    # if algo == 'fbv2' or algo == 'tunas':
+    # if algo == 'mask_gumbel' or algo == 'mask_rl':
     self.register_buffer('_masks', torch.zeros(len(self._candidate_Cs), max(self._candidate_Cs)))
     for i in range(len(self._candidate_Cs)):
       self._masks.data[i, :self._candidate_Cs[i]] = 1
@@ -130,7 +130,7 @@ class GenericNAS301Model(nn.Module):
         else:
           mask = self._masks[random.randint(0, len(self._masks)-1)]
         feature = feature * mask.view(1, -1, 1, 1)
-      elif self._algo == 'fbv2':
+      elif self._algo == 'mask_gumbel':
         weights = nn.functional.gumbel_softmax(self._arch_parameters[idx:idx+1], tau=self.tau, dim=-1)
         mask = torch.matmul(weights, self._masks).view(1, -1, 1, 1)
         feature = feature * mask
@@ -148,7 +148,7 @@ class GenericNAS301Model(nn.Module):
         else:
           miss = torch.zeros(feature.shape[0], feature.shape[1]-out.shape[1], feature.shape[2], feature.shape[3], device=feature.device)
           feature = torch.cat((out, miss), dim=1)
-      elif self._algo == 'tunas':
+      elif self._algo == 'mask_rl':
         prob = nn.functional.softmax(self._arch_parameters[idx:idx+1], dim=-1)
         dist = torch.distributions.Categorical(prob)
         action = dist.sample()
diff --git a/lib/nats_bench/api_utils.py b/lib/nats_bench/api_utils.py
index e12831e..433a1aa 100644
--- a/lib/nats_bench/api_utils.py
+++ b/lib/nats_bench/api_utils.py
@@ -939,9 +939,9 @@ class ArchResults(object):
     x.load_state_dict(state_dict)
     return x
 
-  # This function is used to clear the weights saved in each 'result'
-  # This can help reduce the memory footprint.
   def clear_params(self):
+    """Clear the weights saved in each 'result'."""
+    # NOTE(xuanyidong): This can help reduce the memory footprint.
     for unused_key, result in self.all_results.items():
       del result.net_state_dict
       result.net_state_dict = None
diff --git a/scripts-search/NATS/search-size.sh b/scripts-search/NATS/search-size.sh
index df5b97d..86329f5 100644
--- a/scripts-search/NATS/search-size.sh
+++ b/scripts-search/NATS/search-size.sh
@@ -23,11 +23,11 @@ CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset
 CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --warmup_ratio ${ratio} --rand_seed ${seed}
 
 #
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --warmup_ratio ${ratio} --rand_seed ${seed}
 
 #
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
-CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar10  --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}
+CUDA_VISIBLE_DEVICES=${device} python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_rl --arch_weight_decay 0 --warmup_ratio ${ratio} --rand_seed ${seed}