config: Add weight decay to training configuration

Adds a weight_decay parameter to the TrainConfig and applies it to the AdamW optimizer.

train.py

@@ -29,6 +29,7 @@ class TrainConfig:
     batch_size = 128
     lr_initial = 6e-4
     lr_final = 6e-5
+    weight_decay = 2e-1
     warmup_epochs = 10
     early_stopping_patience = 10
     
@@ -75,7 +76,7 @@ def main():
     print(f"Model initialized with {model.get_num_params():.2f}M trainable parameters.")
 
     loss_fn = CombinedLoss(config.ignored_token_ids)
-    optimizer = AdamW(model.parameters(), lr=config.lr_initial)
+    optimizer = AdamW(model.parameters(), lr=config.lr_initial, weight_decay=config.weight_decay)
 
     # --- 3. Training Loop ---
     best_val_loss = float('inf')