diff --git a/src/transformers/integrations/kt.py b/src/transformers/integrations/kt.py
index 2018f635c540..09419c3e49fe 100644
--- a/src/transformers/integrations/kt.py
+++ b/src/transformers/integrations/kt.py
@@ -48,6 +48,8 @@ class HfTrainerKTConfig:
         "kt_lora_alpha": ("ACCELERATE_KT_LORA_ALPHA", float),
         "kt_model_max_length": ("ACCELERATE_KT_MODEL_MAX_LENGTH", int),
         "kt_skip_expert_loading": ("ACCELERATE_KT_SKIP_EXPERT_LOADING", bool),
+        "kt_train_mode": ("ACCELERATE_KT_TRAIN_MODE", str),
+        "kt_full_weight_grad": ("ACCELERATE_KT_FULL_WEIGHT_GRAD", bool),
     }
 
     def __init__(self, kt_config_dict: Any | None):
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 8f2968b75487..f3403dc862d2 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -242,13 +242,14 @@
     try:
         from kt_kernel.sft import (
             get_kt_lora_params,
+            get_kt_trainable_params,
             kt_adapt_peft_lora,
             load_kt_moe_from_adapter,
             save_kt_moe_to_adapter,
             update_kt_lora_pointers,
         )
     except ImportError:
-        get_kt_lora_params = kt_adapt_peft_lora = load_kt_moe_from_adapter = save_kt_moe_to_adapter = update_kt_lora_pointers = None
+        get_kt_lora_params = get_kt_trainable_params = kt_adapt_peft_lora = load_kt_moe_from_adapter = save_kt_moe_to_adapter = update_kt_lora_pointers = None
 
 
 if TYPE_CHECKING:
@@ -1518,6 +1519,20 @@ def _inner_training_loop(
         logger.info(f"  Total optimization steps = {max_steps:,}")
         logger.info(f"  Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}")
 
+        # Include KT wrapper trainable params (gate_proj_buf etc.) which are invisible
+        # to model.parameters() because BaseSFTMoEWrapper is not nn.Module.
+        if self.is_kt_enabled and get_kt_trainable_params is not None:
+            try:
+                kt_model_for_count = self.accelerator.unwrap_model(model, keep_torch_compile=False)
+                kt_trainable = get_kt_trainable_params(kt_model_for_count)
+                kt_trainable_numel = sum(p.numel() for p in kt_trainable) if kt_trainable else 0
+                if kt_trainable_numel > 0:
+                    logger.info(f"  Number of KT trainable parameters = {kt_trainable_numel:,}")
+                    total_trainable = get_model_param_count(model, trainable_only=True) + kt_trainable_numel
+                    logger.info(f"  Total trainable parameters (model + KT) = {total_trainable:,}")
+            except Exception:
+                pass  # Non-critical: best-effort count
+
         if resume_from_checkpoint is not None:
             logger.info(
                 f"  Resuming training from checkpoint with epoch {epochs_trained} and global step {self.state.global_step}"
@@ -1688,16 +1703,21 @@ def _prepare_for_training(self, max_steps, train_dataloader, resume_from_checkpo
         if kt_model is not None and kt_adapt_peft_lora is not None:
             kt_adapt_peft_lora(kt_model)
 
-            # Inject fused expert LoRA params into existing optimizer's last param group
+            # Inject KT trainable params (LoRA or full weight grad) into existing optimizer's last param group
             # (cannot use add_param_group — lr_scheduler is already created with fixed group count)
-            if self.optimizer is not None and get_kt_lora_params is not None:
-                kt_lora_params = get_kt_lora_params(kt_model)
-                if kt_lora_params:
+            if self.optimizer is not None and get_kt_trainable_params is not None:
+                kt_params = get_kt_trainable_params(kt_model)
+                if kt_params:
                     existing_ids = {id(p) for group in self.optimizer.param_groups for p in group["params"]}
-                    new_params = [p for p in kt_lora_params if id(p) not in existing_ids]
+                    new_params = [p for p in kt_params if id(p) not in existing_ids]
                     if new_params:
                         self.optimizer.param_groups[-1]["params"].extend(new_params)
-                        logger.info(f"Injected {len(new_params)} fused expert LoRA params into optimizer")
+                        has_full_weight_grad = any(
+                            getattr(w, "_full_weight_grad", False)
+                            for w in getattr(kt_model, "_kt_wrappers", [])
+                        )
+                        param_type = "base weight + LoRA" if has_full_weight_grad else "LoRA"
+                        logger.info(f"Injected {len(new_params)} fused expert {param_type} params into optimizer")
 
         # load checkpoint
         if resume_from_checkpoint is not None: