diff --git a/README.md b/README.md
index 6e6b4dac..03350041 100644
--- a/README.md
+++ b/README.md
@@ -136,7 +136,7 @@ module_PF = NeuralNet(
 module_PB = NeuralNet(
     input_dim=env.preprocessor.output_dim,
     output_dim=env.n_actions - 1,
-    torso=module_PF.torso  # We share all the parameters of P_F and P_B, except for the last layer
+    trunk=module_PF.trunk  # We share all the parameters of P_F and P_B, except for the last layer
 )
 module_logF = NeuralNet(
     input_dim=env.preprocessor.output_dim,
diff --git a/testing/test_parametrizations_and_losses.py b/testing/test_parametrizations_and_losses.py
index 9fe0ebcc..a2710364 100644
--- a/testing/test_parametrizations_and_losses.py
+++ b/testing/test_parametrizations_and_losses.py
@@ -157,7 +157,7 @@ def PFBasedGFlowNet_with_return(
                 hidden_dim=32,
                 n_hidden_layers=2,
                 n_components=ndim + 1,
-                torso=pf_module.torso if tie_pb_to_pf else None,
+                trunk=pf_module.trunk if tie_pb_to_pf else None,
             )
         elif module_name == "NeuralNet" and env_name != "Box":
             pb_module = NeuralNet(
diff --git a/testing/test_samplers_and_trajectories.py b/testing/test_samplers_and_trajectories.py
index 3f905046..318ed1d1 100644
--- a/testing/test_samplers_and_trajectories.py
+++ b/testing/test_samplers_and_trajectories.py
@@ -43,7 +43,7 @@ def trajectory_sampling_with_return(
             hidden_dim=32,
             n_hidden_layers=2,
             n_components=n_components,
-            torso=pf_module.torso,
+            trunk=pf_module.trunk,
         )
         pf_estimator = BoxPFEstimator(
             env=env,
diff --git a/tutorials/examples/train_box.py b/tutorials/examples/train_box.py
index b6eeedc6..64dd8e01 100644
--- a/tutorials/examples/train_box.py
+++ b/tutorials/examples/train_box.py
@@ -118,7 +118,7 @@ def main(args):  # noqa: C901
             hidden_dim=args.hidden_dim,
             n_hidden_layers=args.n_hidden,
             n_components=args.n_components,
-            torso=pf_module.torso if args.tied else None,
+            trunk=pf_module.trunk if args.tied else None,
         )
 
     pf_estimator = BoxPFEstimator(
@@ -148,7 +148,7 @@ def main(args):  # noqa: C901
             output_dim=1,
             hidden_dim=args.hidden_dim,
             n_hidden_layers=args.n_hidden,
-            torso=None,  # We do not tie the parameters of the flow function to PF
+            trunk=None,  # We do not tie the parameters of the flow function to PF
             logZ_value=logZ,
         )
         logF_estimator = ScalarEstimator(module=module, preprocessor=env.preprocessor)
diff --git a/tutorials/examples/train_conditional.py b/tutorials/examples/train_conditional.py
index a713465a..057ccd71 100644
--- a/tutorials/examples/train_conditional.py
+++ b/tutorials/examples/train_conditional.py
@@ -25,7 +25,7 @@ def build_conditional_pf_pb(env):
         input_dim=env.preprocessor.output_dim,
         output_dim=CONCAT_SIZE,
         hidden_dim=256,
-        torso=module_PF.torso,
+        trunk=module_PF.trunk,
     )
 
     # Encoder for the Conditioning information.
@@ -43,7 +43,7 @@ def build_conditional_pf_pb(env):
     module_final_PB = NeuralNet(
         input_dim=CONCAT_SIZE * 2,
         output_dim=env.n_actions - 1,
-        torso=module_final_PF.torso,
+        trunk=module_final_PF.trunk,
     )
 
     pf_estimator = ConditionalDiscretePolicyEstimator(
diff --git a/tutorials/examples/train_hypergrid_simple.py b/tutorials/examples/train_hypergrid_simple.py
index d2d5bccc..67464100 100644
--- a/tutorials/examples/train_hypergrid_simple.py
+++ b/tutorials/examples/train_hypergrid_simple.py
@@ -26,7 +26,7 @@
 module_PB = NeuralNet(
     input_dim=env.preprocessor.output_dim,
     output_dim=env.n_actions - 1,
-    torso=module_PF.torso,
+    trunk=module_PF.trunk,
 )
 pf_estimator = DiscretePolicyEstimator(
     module_PF, env.n_actions, is_backward=False, preprocessor=env.preprocessor