diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index 06108660..1b5a9457 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -884,10 +884,17 @@ class AscendMLAImpl(MLAAttentionImpl):
 
         W_UK, W_UV = kv_b_proj_weight.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-        # Convert from (L, N, V) to (N, L, V)
-        self.W_UV = W_UV.transpose(0, 1).contiguous()
-        # Convert from (L, N, P) to (N, P, L)
-        self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+        # NOTE: When we make a incontiguous weight contiguous, a new address will be allocated for the weight,
+        # in graph + RL scenario, we only capture the graph once, and the weight address is expected to be the same
+        # across iterations, so we need to copy the weight to the original address after making it contiguous.
+        if not hasattr(self, "W_UV"):
+            # Convert from (L, N, V) to (N, L, V)
+            self.W_UV = W_UV.transpose(0, 1).contiguous()
+            # Convert from (L, N, P) to (N, P, L)
+            self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+        else:
+            self.W_UV.copy_(W_UV.transpose(0, 1).contiguous())
+            self.W_UK_T.copy_(W_UK.permute(1, 2, 0).contiguous())
 
         # TODO(zzzzwwjj): Currently, torch.ops._C_ascend.batch_matmul_transpose cannot support weight nz
         # self.W_UV = maybe_trans_nz(self.W_UV)
diff --git a/vllm_ascend/attention/sfa_v1.py b/vllm_ascend/attention/sfa_v1.py
index 7d787648..0aec273b 100644
--- a/vllm_ascend/attention/sfa_v1.py
+++ b/vllm_ascend/attention/sfa_v1.py
@@ -483,10 +483,17 @@ class AscendSFAImpl(MLAAttentionImpl):
 
         W_UK, W_UV = kv_b_proj_weight.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-        # Convert from (L, N, V) to (N, L, V)
-        self.W_UV = W_UV.transpose(0, 1).contiguous()
-        # Convert from (L, N, P) to (N, P, L)
-        self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+        # NOTE: When we make a incontiguous weight contiguous, a new address will be allocated for the weight,
+        # in graph + RL scenario, we only capture the graph once, and the weight address is expected to be the same
+        # across iterations, so we need to copy the weight to the original address after making it contiguous.
+        if not hasattr(self, "W_UV"):
+            # Convert from (L, N, V) to (N, L, V)
+            self.W_UV = W_UV.transpose(0, 1).contiguous()
+            # Convert from (L, N, P) to (N, P, L)
+            self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+        else:
+            self.W_UV.copy_(W_UV.transpose(0, 1).contiguous())
+            self.W_UK_T.copy_(W_UK.permute(1, 2, 0).contiguous())
 
         # TODO(zzzzwwjj): Currently, torch.ops._C_ascend.batch_matmul_transpose cannot support weight nz
         # self.W_UV = maybe_trans_nz(self.W_UV)