diff --git a/python/sglang/srt/models/qwen2.py b/python/sglang/srt/models/qwen2.py
index 30b02ec83..0760c4645 100644
--- a/python/sglang/srt/models/qwen2.py
+++ b/python/sglang/srt/models/qwen2.py
@@ -457,20 +457,6 @@ class Qwen2ForCausalLM(nn.Module):
             # ranks other than the last rank will have a placeholder layer
             self.lm_head = PPMissingLayer()
 
-        # perform weight tying for PP
-        if self.pp_group.world_size > 1 and config.tie_word_embeddings:
-            if self.pp_group.is_first_rank:
-                self.pp_group.send(
-                    self.model.embed_tokens.weight, dst=self.pp_group.last_rank
-                )
-            elif self.pp_group.is_last_rank:
-                emb_token_weight = self.pp_group.recv(
-                    size=(config.vocab_size, config.hidden_size),
-                    dtype=next(self.model.parameters()).dtype,
-                    src=self.pp_group.first_rank,
-                )
-                self.lm_head.weight.copy_(emb_token_weight)
-
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
         # For EAGLE3 support
@@ -589,22 +575,21 @@ class Qwen2ForCausalLM(nn.Module):
             ):
                 continue
 
+            if name == "model.embed_tokens.weight":
+                if self.pp_group.is_last_rank and self.config.tie_word_embeddings:
+                    if "lm_head.weight" in params_dict:
+                        param = params_dict["lm_head.weight"]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+
             if "rotary_emb.inv_freq" in name or "projector" in name:
                 continue
             if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
                 continue
-            if self.config.tie_word_embeddings and "lm_head.weight" in name:
-                if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
-                    # Handle pp weight tying here
-                    # find the embed_tokens.weight in the weights
-                    embed_token_weights = next(
-                        filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
-                    )[1]
-                    loaded_weight = embed_token_weights
-                else:
-                    continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue
 
diff --git a/python/sglang/srt/models/qwen3.py b/python/sglang/srt/models/qwen3.py
index a3d5fc4a4..f0ee994a1 100644
--- a/python/sglang/srt/models/qwen3.py
+++ b/python/sglang/srt/models/qwen3.py
@@ -379,20 +379,6 @@ class Qwen3ForCausalLM(nn.Module):
             # ranks other than the last rank will have a placeholder layer
             self.lm_head = PPMissingLayer()
 
-        # perform weight tying for PP
-        if self.pp_group.world_size > 1 and config.tie_word_embeddings:
-            if self.pp_group.is_first_rank:
-                self.pp_group.send(
-                    self.model.embed_tokens.weight, dst=self.pp_group.world_size - 1
-                )
-            elif self.pp_group.is_last_rank:
-                emb_token_weight = self.pp_group.recv(
-                    size=self.lm_head.weight.shape,
-                    dtype=next(self.model.parameters()).dtype,
-                    src=0,
-                )
-                self.lm_head.weight.copy_(emb_token_weight)
-
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
@@ -501,6 +487,16 @@ class Qwen3ForCausalLM(nn.Module):
         for name, loaded_weight in weights:
             if "Embedding" in self.config.name_or_path:
                 name = add_prefix(name, "model")
+
+            if name == "model.embed_tokens.weight":
+                if self.pp_group.is_last_rank and self.config.tie_word_embeddings:
+                    if "lm_head.weight" in params_dict:
+                        param = params_dict["lm_head.weight"]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+
             layer_id = get_layer_id(name)
             if (
                 layer_id is not None
@@ -518,16 +514,6 @@ class Qwen3ForCausalLM(nn.Module):
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
                 continue
-            if self.config.tie_word_embeddings and "lm_head.weight" in name:
-                if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
-                    # Handle pp weight tying here
-                    # find the embed_tokens.weight in the weights
-                    embed_token_weights = next(
-                        filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
-                    )[1]
-                    loaded_weight = embed_token_weights
-                else:
-                    continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue
             if "scale" in name: