diff --git a/model_training_nnn_tpu/trainer_tf.py b/model_training_nnn_tpu/trainer_tf.py
index 88f6df7..c4e97d1 100644
--- a/model_training_nnn_tpu/trainer_tf.py
+++ b/model_training_nnn_tpu/trainer_tf.py
@@ -137,12 +137,14 @@ class BrainToTextDecoderTrainerTF:
         self.adv_noise_l2_weight = float(adv_cfg.get('noise_l2_weight', 0.0))
         self.adv_warmup_steps = int(adv_cfg.get('warmup_steps', 0))
 
-        # TPU-specific weight decay handling
+        # Manual weight decay handling for all environments (since we use Adam)
         self.manual_weight_decay = False
-        if isinstance(self.strategy, tf.distribute.TPUStrategy) and self.args.get('weight_decay', 0.0) > 0:
+        if self.args.get('weight_decay', 0.0) > 0:
             self.manual_weight_decay = True
             self.weight_decay_rate = self.args['weight_decay']
             print(f"🔧 Manual L2 regularization enabled: {self.weight_decay_rate}")
+        else:
+            print("💡 No weight decay configured")
 
         if self.adv_enabled:
             if self.logger:
@@ -435,28 +437,19 @@ class BrainToTextDecoderTrainerTF:
 
         # For TPU training, we need to be more explicit about optimizer configuration
         # to avoid strategy context issues
-        if isinstance(self.strategy, tf.distribute.TPUStrategy):
-            print("Using TPU-optimized optimizer configuration")
-            # TPU-specific optimizer configuration
-            # IMPORTANT: Disable weight_decay for TPU due to distributed training compatibility issues
-            # We'll implement manual L2 regularization instead
-            optimizer = tf.keras.optimizers.AdamW(
-                learning_rate=self.args['lr_max'],
-                beta_1=self.args['beta0'],
-                beta_2=self.args['beta1'],
-                epsilon=self.args['epsilon'],
-                weight_decay=0.0  # Disabled for TPU compatibility
-                # REMOVE global_clipnorm to avoid double clipping with manual tf.clip_by_global_norm
-            )
-        else:
-            print("Using standard optimizer configuration")
-            optimizer = tf.keras.optimizers.AdamW(
-                learning_rate=self.args['lr_max'],
-                beta_1=self.args['beta0'],
-                beta_2=self.args['beta1'],
-                epsilon=self.args['epsilon'],
-                weight_decay=self.args['weight_decay']
-            )
+        # IMPORTANT: Use Adam instead of AdamW to avoid TPU distributed training bugs
+        # AdamW has known issues with _apply_weight_decay in TPU environments even when weight_decay=0.0
+        # We implement manual L2 regularization (weight decay) in the training step instead
+        print("Using TPU-compatible Adam optimizer (avoiding AdamW distributed training bugs)")
+        print("💡 Manual L2 regularization will be applied in training step")
+
+        optimizer = tf.keras.optimizers.Adam(
+            learning_rate=self.args['lr_max'],
+            beta_1=self.args['beta0'],
+            beta_2=self.args['beta1'],
+            epsilon=self.args['epsilon']
+            # No weight_decay parameter in Adam - handled manually
+        )
 
         return optimizer