diff --git a/verl/models/transformers/qwen3_vl.py b/verl/models/transformers/qwen3_vl.py
index 972848a1a08..55888182c96 100644
--- a/verl/models/transformers/qwen3_vl.py
+++ b/verl/models/transformers/qwen3_vl.py
@@ -15,6 +15,7 @@
 import functools
 import logging
 import os
+import inspect
 from dataclasses import dataclass
 from typing import Optional
 
@@ -373,3 +374,91 @@ def patched_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
     # Apply the patch
     Qwen3VLMoeTextSparseMoeBlock.forward = patched_forward
     logger.info("Monkey patched Qwen3VLMoeTextSparseMoeBlock.forward to fix router_weights bug")
+
+
+def patch_qwen3_vl_moe_fast_pos_embed_interpolate():
+    """
+    Monkey patch to fix FSDP param_offload device mismatch on NPU.
+    When FSDP offloads params, self.pos_embed.weight.device falls back to CPU, 
+    causing indices to be generated on CPU while computations are executed on NPU.
+    """
+    try:
+        from transformers.models.qwen3_vl_moe import modeling_qwen3_vl_moe
+    except ImportError:
+        return
+
+    def patched_fast_pos_embed_interpolate(self, grid_thw):
+        grid_ts, grid_hs, grid_ws = grid_thw[:, 0], grid_thw[:, 1], grid_thw[:, 2]
+
+        idx_list = [[] for _ in range(4)]
+        weight_list = [[] for _ in range(4)]
+
+        for t, h, w in zip(grid_ts, grid_hs, grid_ws):
+            h_idxs = torch.linspace(0, self.num_grid_per_side - 1, h)
+            w_idxs = torch.linspace(0, self.num_grid_per_side - 1, w)
+
+            h_idxs_floor = h_idxs.int()
+            w_idxs_floor = w_idxs.int()
+            h_idxs_ceil = (h_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+            w_idxs_ceil = (w_idxs.int() + 1).clip(max=self.num_grid_per_side - 1)
+
+            dh = h_idxs - h_idxs_floor
+            dw = w_idxs - w_idxs_floor
+
+            base_h = h_idxs_floor * self.num_grid_per_side
+            base_h_ceil = h_idxs_ceil * self.num_grid_per_side
+
+            indices = [
+                (base_h[None].T + w_idxs_floor[None]).flatten(),
+                (base_h[None].T + w_idxs_ceil[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_floor[None]).flatten(),
+                (base_h_ceil[None].T + w_idxs_ceil[None]).flatten(),
+            ]
+
+            weights = [
+                ((1 - dh)[None].T * (1 - dw)[None]).flatten(),
+                ((1 - dh)[None].T * dw[None]).flatten(),
+                (dh[None].T * (1 - dw)[None]).flatten(),
+                (dh[None].T * dw[None]).flatten(),
+            ]
+
+            for i in range(4):
+                idx_list[i].extend(indices[i].tolist())
+                weight_list[i].extend(weights[i].tolist())
+
+        # BUG FIX: Use grid_thw.device instead of self.pos_embed.weight.device
+        # grid_thw is guaranteed to be on the correct execution device (NPU) during the forward pass.
+        target_device = grid_thw.device
+        
+        idx_tensor = torch.tensor(idx_list, dtype=torch.long, device=target_device)
+        weight_tensor = torch.tensor(
+            weight_list, dtype=self.pos_embed.weight.dtype, device=target_device
+        )
+        pos_embeds = self.pos_embed(idx_tensor) * weight_tensor[:, :, None]
+        patch_pos_embeds = pos_embeds[0] + pos_embeds[1] + pos_embeds[2] + pos_embeds[3]
+
+        patch_pos_embeds = patch_pos_embeds.split([h * w for h, w in zip(grid_hs, grid_ws)])
+
+        patch_pos_embeds_permute = []
+        merge_size = self.config.spatial_merge_size
+        for pos_embed, t, h, w in zip(patch_pos_embeds, grid_ts, grid_hs, grid_ws):
+            pos_embed = pos_embed.repeat(t, 1)
+            pos_embed = (
+                pos_embed.view(t, h // merge_size, merge_size, w // merge_size, merge_size, -1)
+                .permute(0, 1, 3, 2, 4, 5)
+                .flatten(0, 4)
+            )
+            patch_pos_embeds_permute.append(pos_embed)
+        patch_pos_embeds = torch.cat(patch_pos_embeds_permute)
+        return patch_pos_embeds
+
+    # Dynamically inject the patch into any Vision Class holding this method
+    for name, obj in inspect.getmembers(modeling_qwen3_vl_moe, inspect.isclass):
+        if hasattr(obj, 'fast_pos_embed_interpolate'):
+            setattr(obj, 'fast_pos_embed_interpolate', patched_fast_pos_embed_interpolate)
+            logger.info(f"Monkey patched {name}.fast_pos_embed_interpolate to fix FSDP param_offload NPU device bug")
+
+
+# Execute patches upon module import
+patch_qwen3_vl_moe_sparse_moe_block_forward()
+patch_qwen3_vl_moe_fast_pos_embed_interpolate()