diff --git a/PyFlyt/gym_envs/__init__.py b/PyFlyt/gym_envs/__init__.py
index b1d648a9..ace30c75 100644
--- a/PyFlyt/gym_envs/__init__.py
+++ b/PyFlyt/gym_envs/__init__.py
@@ -6,11 +6,11 @@
 
 # QuadX Envs
 register(
-    id="PyFlyt/QuadX-Hover-v3",
+    id="PyFlyt/QuadX-Hover-v4",
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_hover_env:QuadXHoverEnv",
 )
 register(
-    id="PyFlyt/QuadX-Waypoints-v3",
+    id="PyFlyt/QuadX-Waypoints-v4",
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_waypoints_env:QuadXWaypointsEnv",
 )
 register(
@@ -18,15 +18,15 @@
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_gates_env:QuadXGatesEnv",
 )
 register(
-    id="PyFlyt/QuadX-Pole-Balance-v3",
+    id="PyFlyt/QuadX-Pole-Balance-v4",
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_pole_balance_env:QuadXPoleBalanceEnv",
 )
 register(
-    id="PyFlyt/QuadX-Pole-Waypoints-v3",
+    id="PyFlyt/QuadX-Pole-Waypoints-v4",
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_pole_waypoints_env:QuadXPoleWaypointsEnv",
 )
 register(
-    id="PyFlyt/QuadX-Ball-In-Cup-v3",
+    id="PyFlyt/QuadX-Ball-In-Cup-v4",
     entry_point="PyFlyt.gym_envs.quadx_envs.quadx_ball_in_cup_env:QuadXBallInCupEnv",
 )
 
diff --git a/PyFlyt/gym_envs/quadx_envs/quadx_ball_in_cup_env.py b/PyFlyt/gym_envs/quadx_envs/quadx_ball_in_cup_env.py
index 523dbc03..50e268a3 100644
--- a/PyFlyt/gym_envs/quadx_envs/quadx_ball_in_cup_env.py
+++ b/PyFlyt/gym_envs/quadx_envs/quadx_ball_in_cup_env.py
@@ -255,7 +255,6 @@ def compute_state(self) -> None:
     def compute_term_trunc_reward(self) -> None:
         """Computes the termination, truncation, and reward of the current timestep."""
         super().compute_base_term_trunc_reward()
-
         # compute some parameters of the ball
         # lin_pos: [3,], height: [1,], abs_dist: [1,]
         ball_rel_lin_pos = self.ball_lin_pos - self.env.state(0)[-1]
@@ -264,6 +263,15 @@ def compute_term_trunc_reward(self) -> None:
 
         # bonus reward if we are not sparse
         if not self.sparse_reward:
+            # Negative Reward For High Yaw rate, To prevent high yaw while training
+            yaw_rate = abs(
+                self.env.state(0)[0][2]
+            )  # Assuming z-axis is the last component
+            yaw_rate_penalty = 0.01 * yaw_rate**2  # Add penalty for high yaw rate
+            self.reward -= (
+                yaw_rate_penalty  # You can adjust the coefficient (0.01) as needed
+            )
+
             # reward for staying alive
             self.reward += 0.4
 
diff --git a/PyFlyt/gym_envs/quadx_envs/quadx_hover_env.py b/PyFlyt/gym_envs/quadx_envs/quadx_hover_env.py
index ae535386..9b80f25f 100644
--- a/PyFlyt/gym_envs/quadx_envs/quadx_hover_env.py
+++ b/PyFlyt/gym_envs/quadx_envs/quadx_hover_env.py
@@ -117,12 +117,19 @@ def compute_state(self) -> None:
     def compute_term_trunc_reward(self) -> None:
         """Computes the termination, truncation, and reward of the current timestep."""
         super().compute_base_term_trunc_reward()
-
         if not self.sparse_reward:
             # distance from 0, 0, 1 hover point
             linear_distance = np.linalg.norm(
                 self.env.state(0)[-1] - np.array([0.0, 0.0, 1.0])
             )
+            # Negative Reward For High Yaw rate, To prevent high yaw while training
+            yaw_rate = abs(
+                self.env.state(0)[0][2]
+            )  # Assuming z-axis is the last component
+            yaw_rate_penalty = 0.01 * yaw_rate**2  # Add penalty for high yaw rate
+            self.reward -= (
+                yaw_rate_penalty  # You can adjust the coefficient (0.01) as needed
+            )
 
             # how far are we from 0 roll pitch
             angular_distance = np.linalg.norm(self.env.state(0)[1][:2])
diff --git a/PyFlyt/gym_envs/quadx_envs/quadx_pole_balance_env.py b/PyFlyt/gym_envs/quadx_envs/quadx_pole_balance_env.py
index 6c2ec3f7..e3a885dd 100644
--- a/PyFlyt/gym_envs/quadx_envs/quadx_pole_balance_env.py
+++ b/PyFlyt/gym_envs/quadx_envs/quadx_pole_balance_env.py
@@ -184,7 +184,15 @@ def compute_term_trunc_reward(self) -> None:
 
             # how far are we from 0 roll pitch
             angular_distance = np.linalg.norm(self.env.state(0)[1][:2])
-
             self.reward -= linear_distance + angular_distance
             self.reward -= self.pole.leaningness
             self.reward += 1.0
+
+            # Negative Reward For High Yaw rate, To prevent high yaw while training
+            yaw_rate = abs(
+                self.env.state(0)[0][2]
+            )  # Assuming z-axis is the last component
+            yaw_rate_penalty = 0.01 * yaw_rate**2  # Add penalty for high yaw rate
+            self.reward -= (
+                yaw_rate_penalty  # You can adjust the coefficient (0.01) as needed
+            )
diff --git a/PyFlyt/gym_envs/quadx_envs/quadx_pole_waypoints_env.py b/PyFlyt/gym_envs/quadx_envs/quadx_pole_waypoints_env.py
index 953ed24a..523f3d2e 100644
--- a/PyFlyt/gym_envs/quadx_envs/quadx_pole_waypoints_env.py
+++ b/PyFlyt/gym_envs/quadx_envs/quadx_pole_waypoints_env.py
@@ -224,12 +224,19 @@ def compute_state(self) -> None:
     def compute_term_trunc_reward(self) -> None:
         """Computes the termination, truncation, and reward of the current timestep."""
         super().compute_base_term_trunc_reward()
-
         # bonus reward if we are not sparse
         if not self.sparse_reward:
             self.reward += max(15.0 * self.waypoints.progress_to_next_target, 0.0)
             self.reward += 0.5 / self.waypoints.distance_to_next_target
             self.reward += 0.5 - self.pole.leaningness
+            # Negative Reward For High Yaw rate, To prevent high yaw while training
+            yaw_rate = abs(
+                self.env.state(0)[0][2]
+            )  # Assuming z-axis is the last component
+            yaw_rate_penalty = 0.01 * yaw_rate**2  # Add penalty for high yaw rate
+            self.reward -= (
+                yaw_rate_penalty  # You can adjust the coefficient (0.01) as needed
+            )
 
         # target reached
         if self.waypoints.target_reached:
diff --git a/PyFlyt/gym_envs/quadx_envs/quadx_waypoints_env.py b/PyFlyt/gym_envs/quadx_envs/quadx_waypoints_env.py
index 3e633cdc..8b4c7a39 100644
--- a/PyFlyt/gym_envs/quadx_envs/quadx_waypoints_env.py
+++ b/PyFlyt/gym_envs/quadx_envs/quadx_waypoints_env.py
@@ -182,6 +182,14 @@ def compute_term_trunc_reward(self) -> None:
         if not self.sparse_reward:
             self.reward += max(3.0 * self.waypoints.progress_to_next_target, 0.0)
             self.reward += 0.1 / self.waypoints.distance_to_next_target
+            # Negative Reward For High Yaw rate, To prevent high yaw while training
+            yaw_rate = abs(
+                self.env.state(0)[0][2]
+            )  # Assuming z-axis is the last component
+            yaw_rate_penalty = 0.01 * yaw_rate**2  # Add penalty for high yaw rate
+            self.reward -= (
+                yaw_rate_penalty  # You can adjust the coefficient (0.01) as needed
+            )
 
         # target reached
         if self.waypoints.target_reached:
diff --git a/tests/test_gym_envs.py b/tests/test_gym_envs.py
index 0879d0b5..92fa83cc 100644
--- a/tests/test_gym_envs.py
+++ b/tests/test_gym_envs.py
@@ -16,8 +16,8 @@
 _WAYPOINT_ENV_CONFIGS = []
 for env_name, angle_representation, sparse_reward in itertools.product(
     [
-        "PyFlyt/QuadX-Waypoints-v3",
-        "PyFlyt/QuadX-Pole-Waypoints-v3",
+        "PyFlyt/QuadX-Waypoints-v4",
+        "PyFlyt/QuadX-Pole-Waypoints-v4",
         "PyFlyt/Fixedwing-Waypoints-v3",
     ],
     ["euler", "quaternion"],
@@ -37,9 +37,9 @@
 _NORMAL_ENV_CONFIGS = []
 for env_name, angle_representation, sparse_reward in itertools.product(
     [
-        "PyFlyt/QuadX-Hover-v3",
-        "PyFlyt/QuadX-Pole-Balance-v3",
-        "PyFlyt/QuadX-Ball-In-Cup-v3",
+        "PyFlyt/QuadX-Hover-v4",
+        "PyFlyt/QuadX-Pole-Balance-v4",
+        "PyFlyt/QuadX-Ball-In-Cup-v4",
         "PyFlyt/Rocket-Landing-v4",
     ],
     ["euler", "quaternion"],