diff --git a/ding/policy/dqfd.py b/ding/policy/dqfd.py
index 22108b6ebced0754ffe5a1116e18d086a640f564..d64e75f25e37f53727362b0564af5646f4d659d1 100644
--- a/ding/policy/dqfd.py
+++ b/ding/policy/dqfd.py
@@ -209,7 +209,7 @@ class DQFDPolicy(DQNPolicy):
             data['is_expert']  # set is_expert flag(expert 1, agent 0)
         )
         value_gamma = data.get('value_gamma')
-        loss, td_error_per_sample = dqfd_nstep_td_error(
+        loss, td_error_per_sample, loss_statistics = dqfd_nstep_td_error(
             data_n,
             self._gamma,
             self.lambda1,
diff --git a/ding/rl_utils/tests/test_td.py b/ding/rl_utils/tests/test_td.py
index 06fbe60d2d8e974dc6a7dc4cb6e8a6f2ce8f6392..3fd32cf0af4efe5630c403a6d87e326d339eb9af 100644
--- a/ding/rl_utils/tests/test_td.py
+++ b/ding/rl_utils/tests/test_td.py
@@ -253,7 +253,7 @@ def test_dqfd_nstep_td():
         data = dqfd_nstep_td_data(
             q, next_q, action, next_action, reward, done, done_1, None, next_q_one_step, next_action_one_step, is_expert
         )
-        loss, td_error_per_sample = dqfd_nstep_td_error(
+        loss, td_error_per_sample, loss_statistics = dqfd_nstep_td_error(
             data, 0.95, lambda_n_step_td=1, lambda_supervised_loss=1, margin_function=0.8, nstep=nstep
         )
         assert td_error_per_sample.shape == (batch_size, )