diff --git a/.gitignore b/.gitignore
index 5557c585..452ec69b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -18,3 +18,4 @@ aic_utils/aic_isaac/aic_isaaclab/source/aic_task/aic_task/tasks/manager_based/ai
 
 # Training checkpoints (too large for git)
 checkpoints/
+wandb/
diff --git a/aic_example_policies/aic_example_policies/ros/RunRLT.py b/aic_example_policies/aic_example_policies/ros/RunRLT.py
index 1d88acd1..3deb5e83 100644
--- a/aic_example_policies/aic_example_policies/ros/RunRLT.py
+++ b/aic_example_policies/aic_example_policies/ros/RunRLT.py
@@ -139,6 +139,8 @@ def __init__(self, parent_node: Node):
             ("policy_args.prompt_align",     _PHASE_PROMPT_DEFAULTS["align"]),
             ("policy_args.prompt_insert",    _PHASE_PROMPT_DEFAULTS["insert"]),
             ("policy_args.prompt_verify",    _PHASE_PROMPT_DEFAULTS["verify"]),
+            # Debug: bypass actor and execute VLA reference actions directly.
+            ("policy_args.vla_only",         False),
         ]
         for name, default in _params:
             try:
@@ -159,6 +161,10 @@ def __init__(self, parent_node: Node):
             "insert":   parent_node.get_parameter("policy_args.prompt_insert").value,
             "verify":   parent_node.get_parameter("policy_args.prompt_verify").value,
         }
+        self._vla_only = parent_node.get_parameter("policy_args.vla_only").value
+        if self._vla_only:
+            self.get_logger().info("VLA-only mode: bypassing actor, executing VLA refs directly")
+
         port_pose = list(parent_node.get_parameter("policy_args.port_pose_xyzquat").value)
         if len(port_pose) == 7:
             self._port_pos = np.asarray(port_pose[0:3], dtype=np.float64)
@@ -385,6 +391,8 @@ def _estimate_phase(self, prop: np.ndarray) -> str:
 
     def _maybe_switch_prompt(self, phase: str) -> None:
         """Call backend.set_instruction when the phase changes."""
+        if self._port_pos is None:
+            return
         if phase == self._current_phase:
             return
         prompt = self._phase_prompts.get(phase)
@@ -397,6 +405,43 @@ def _maybe_switch_prompt(self, phase: str) -> None:
         except Exception as e:
             self.get_logger().warn(f"set_instruction failed on phase={phase}: {e}")
 
+    # Max position delta per control step (m). Prevents large jumps that
+    # generate torques exceeding Gazebo's effort limits.
+    _MAX_POS_DELTA: float = 0.005   # 5 mm per step @ 20 Hz = 10 cm/s max
+    _MAX_ROT_DELTA: float = 0.05    # ~3° per step @ 20 Hz = 60°/s max
+
+    def _clamp_action(self, action: np.ndarray, obs: "Observation") -> np.ndarray:
+        """Clamp action so the commanded pose is close to the current TCP pose.
+
+        CheatCode works because it interpolates smoothly over 100 steps.
+        Without clamping, the actor can command a pose far from the current
+        one → huge joint torques → effort clamped to 0 → arm collapses.
+        """
+        current_pos = np.array([
+            obs.controller_state.tcp_pose.position.x,
+            obs.controller_state.tcp_pose.position.y,
+            obs.controller_state.tcp_pose.position.z,
+        ], dtype=np.float32)
+
+        # Clamp position delta
+        pos_delta = action[0:3] - current_pos
+        pos_norm = np.linalg.norm(pos_delta)
+        if pos_norm > self._MAX_POS_DELTA:
+            action = action.copy()
+            action[0:3] = current_pos + pos_delta * (self._MAX_POS_DELTA / pos_norm)
+
+        # Clamp rotation delta (in 6D space — limit the norm of the change)
+        from aic_rlt.vla.xvla_wrapper import quat_to_rot6d
+        q = obs.controller_state.tcp_pose.orientation
+        current_rot6d = quat_to_rot6d(np.array([q.x, q.y, q.z, q.w], dtype=np.float32))
+        rot_delta = action[3:9] - current_rot6d
+        rot_norm = np.linalg.norm(rot_delta)
+        if rot_norm > self._MAX_ROT_DELTA:
+            action = action.copy() if not action.flags.writeable else action
+            action[3:9] = current_rot6d + rot_delta * (self._MAX_ROT_DELTA / rot_norm)
+
+        return action
+
     def _action_to_motion_update(self, action: np.ndarray) -> MotionUpdate:
         """Convert a 9-dim action [xyz, r1, r2] to a MotionUpdate.
 
@@ -417,11 +462,12 @@ def _action_to_motion_update(self, action: np.ndarray) -> MotionUpdate:
                 z=float(quat[2]), w=float(quat[3]),
             ),
         )
+        # Match CheatCode defaults (policy.py:set_pose_target)
         motion_update.target_stiffness = np.diag(
-            [85.0, 85.0, 85.0, 50.0, 50.0, 50.0]
+            [90.0, 90.0, 90.0, 50.0, 50.0, 50.0]
         ).flatten().tolist()
         motion_update.target_damping = np.diag(
-            [75.0, 75.0, 75.0, 20.0, 20.0, 20.0]
+            [50.0, 50.0, 50.0, 20.0, 20.0, 20.0]
         ).flatten().tolist()
         motion_update.feedforward_wrench_at_tip = Wrench(
             force=Vector3(x=0.0, y=0.0, z=0.0),
@@ -471,14 +517,19 @@ def insert_cable(
                 self._maybe_switch_prompt(self._estimate_phase(prop_np))
 
                 z_rl, prop, ref_chunk = self._encode_rl_state(obs)
-                with torch.no_grad():
-                    action_chunk_t = self.actor.get_mean(z_rl, prop, ref_chunk)  # (1, C, D)
-                action_chunk = action_chunk_t.squeeze(0).cpu().numpy()  # (C, D)
+                if self._vla_only:
+                    # Execute VLA reference actions directly (debug mode)
+                    action_chunk = ref_chunk.squeeze(0).cpu().numpy()  # (C, D)
+                else:
+                    with torch.no_grad():
+                        action_chunk_t = self.actor.get_mean(z_rl, prop, ref_chunk)  # (1, C, D)
+                    action_chunk = action_chunk_t.squeeze(0).cpu().numpy()  # (C, D)
                 chunk_step = 0
 
             action = action_chunk[chunk_step]
             chunk_step += 1
 
+            action = self._clamp_action(action, obs)
             move_robot(motion_update=self._action_to_motion_update(action))
             send_feedback(f"RLT step {chunk_step}/{self.CHUNK_LENGTH}")
 
diff --git a/aic_utils/aic_rlt/aic_rlt/trainer.py b/aic_utils/aic_rlt/aic_rlt/trainer.py
index fd8c6070..289211be 100644
--- a/aic_utils/aic_rlt/aic_rlt/trainer.py
+++ b/aic_utils/aic_rlt/aic_rlt/trainer.py
@@ -155,7 +155,7 @@ class RLTConfig:
     # TD3 target network EMA coefficient
     tau: float = 0.005
     # BC regularizer coefficient β (equation (5))
-    bc_coeff: float = 5.0
+    bc_coeff: float = 0.0
     # TD3 target policy noise
     target_policy_noise: float = 0.2
     target_noise_clip: float = 0.5
diff --git a/aic_utils/aic_rlt/scripts/train.py b/aic_utils/aic_rlt/scripts/train.py
index 7bf3ec40..9bc6e9ca 100644
--- a/aic_utils/aic_rlt/scripts/train.py
+++ b/aic_utils/aic_rlt/scripts/train.py
@@ -212,7 +212,7 @@ def parse_args():
     parser.add_argument("--load_checkpoint", type=str, default="")
     parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
     # Hyperparameter overrides
-    parser.add_argument("--bc_coeff", type=float, default=5.0)
+    parser.add_argument("--bc_coeff", type=float, default=0.0)
     parser.add_argument("--n_warmup_steps", type=int, default=2000)
     parser.add_argument("--total_env_steps", type=int, default=50000)
     parser.add_argument("--hidden_dims", type=int, nargs="+", default=[256, 256])