diff --git a/ding/model/template/q_learning.py b/ding/model/template/q_learning.py
index e7d6e7a395..eaec8fc45b 100644
--- a/ding/model/template/q_learning.py
+++ b/ding/model/template/q_learning.py
@@ -1118,8 +1118,9 @@ def __init__(
             gru_bias=gru_bias,
         )
 
+        # for vector obs, use Identity Encoder, i.e. pass
         if isinstance(obs_shape, int) or len(obs_shape) == 1:
-            raise NotImplementedError("not support obs_shape for pre-defined encoder: {}".format(obs_shape))
+            pass
         # replace the embedding layer of Transformer with Conv Encoder
         elif len(obs_shape) == 3:
             assert encoder_hidden_size_list[-1] == hidden_size
diff --git a/ding/policy/r2d2_gtrxl.py b/ding/policy/r2d2_gtrxl.py
index 73b89239f3..9d67289685 100644
--- a/ding/policy/r2d2_gtrxl.py
+++ b/ding/policy/r2d2_gtrxl.py
@@ -55,11 +55,9 @@ class R2D2GTrXLPolicy(Policy):
            | ``done``                                   | calculation.                           | fake termination env
         15 ``collect.n_sample`` int      [8, 128]       | The number of training samples of a    | It varies from
                                                         | call of collector.                     | different envs
-        16 | ``collect.unroll`` int      25             | unroll length of an iteration          | unroll_len>1
+        16 | ``collect.seq``    int      20             | Training sequence length               | unroll_len>=seq_len>1
            | ``_len``
-        17 | ``collect.seq``    int      20             | Training sequence length               | unroll_len>=seq_len>1
-           | ``_len``
-        18 | ``learn.init_``    str      zero           | 'zero' or 'old', how to initialize the |
+        17 | ``learn.init_``    str      zero           | 'zero' or 'old', how to initialize the |
            | ``memory``                                 | memory before each training iteration. |
         == ==================== ======== ============== ======================================== =======================
     """
@@ -81,7 +79,7 @@ class R2D2GTrXLPolicy(Policy):
         discount_factor=0.99,
         # (int) N-step reward for target q_value estimation
         nstep=5,
-        # how many steps to use as burnin
+        # (int) How many steps to use in burnin phase
         burnin_step=1,
         # (int) trajectory length
         unroll_len=25,
@@ -158,7 +156,7 @@ def _init_learn(self) -> None:
         self._seq_len = self._cfg.seq_len
         self._value_rescale = self._cfg.learn.value_rescale
         self._init_memory = self._cfg.learn.init_memory
-        assert self._init_memory in ['zero', 'old']
+        assert self._init_memory in ['zero', 'old'], self._init_memory
 
         self._target_model = copy.deepcopy(self._model)
 
@@ -352,7 +350,6 @@ def _init_collect(self) -> None:
             Collect mode init method. Called by ``self.__init__``.
             Init unroll length and sequence len, collect model.
         """
-        assert 'unroll_len' not in self._cfg.collect, "Use default unroll_len"
         self._nstep = self._cfg.nstep
         self._gamma = self._cfg.discount_factor
         self._unroll_len = self._cfg.unroll_len
diff --git a/dizoo/classic_control/cartpole/config/cartpole_r2d2_gtrxl_config.py b/dizoo/classic_control/cartpole/config/cartpole_r2d2_gtrxl_config.py
index 5a2cdac020..a165245fc1 100644
--- a/dizoo/classic_control/cartpole/config/cartpole_r2d2_gtrxl_config.py
+++ b/dizoo/classic_control/cartpole/config/cartpole_r2d2_gtrxl_config.py
@@ -18,11 +18,11 @@
             obs_shape=4,
             action_shape=2,
             memory_len=5,  # length of transformer memory (can be 0)
-            hidden_size=256,
+            hidden_size=64,
             gru_bias=2.,
             att_layer_num=3,
             dropout=0.,
-            att_head_num=8,
+            att_head_num=4,
         ),
         discount_factor=0.99,
         nstep=3,
@@ -31,7 +31,7 @@
         seq_len=8,  # transformer input segment
         # training sequence: unroll_len - burnin_step - nstep
         learn=dict(
-            update_per_collect=8,
+            update_per_collect=16,
             batch_size=64,
             learning_rate=0.0005,
             target_update_freq=500,