diff --git a/examples/models/vlm/qwen3_vl/README.md b/examples/models/vlm/qwen3_vl/README.md
index da9da325a1..b93438e402 100644
--- a/examples/models/vlm/qwen3_vl/README.md
+++ b/examples/models/vlm/qwen3_vl/README.md
@@ -13,13 +13,16 @@ export WORKSPACE=/your/custom/path
 ```
 
 Directory structure:
+
 - `${WORKSPACE}/models/` - Converted checkpoints
 - `${WORKSPACE}/results/` - Training outputs and experiment results
 
 ## Checkpoint Conversion
 
 ### Import HF → Megatron
+
 To import the HF VL model to your desired Megatron path:
+
 ```bash
 python examples/conversion/convert_checkpoints.py import \
   --hf-model Qwen/Qwen3-VL-8B-Instruct \
@@ -27,6 +30,7 @@ python examples/conversion/convert_checkpoints.py import \
 ```
 
 ### Export Megatron → HF
+
 ```bash
 python examples/conversion/convert_checkpoints.py export \
   --hf-model Qwen/Qwen3-VL-8B-Instruct \
@@ -48,15 +52,18 @@ python -m torch.distributed.run --nproc_per_node=4 examples/conversion/hf_to_meg
 ```
 
 Note:
+
 - `--megatron_model_path` is optional. If not specified, the script will convert the model and then run forward.
 - You can also use image URLs: `--image_path="https://example.com/image.jpg"`
 
 See the [inference.sh](inference.sh) script for commands to:
+
 - Run inference with Hugging Face checkpoints
 - Run inference with imported Megatron checkpoints
 - Run inference with exported Hugging Face checkpoints
 
 **Expected output:**
+
 ```
 ...
 Generation step 46
@@ -88,8 +95,9 @@ Here is a breakdown of the key specifications:
   - `qwen3_vl_8b_finetune_config`: Finetuning for 8B VL model with PEFT support
   - `qwen3_vl_30b_a3b_finetune_config`: Finetuning for 30B-A3B VL model with PEFT support
   - `qwen3_vl_235b_a22b_finetune_config`: Finetuning for 235B-A22B VL model with PEFT support
-    
+
 Before training, ensure the following environment variables are set:
+
 1. `HF_TOKEN`: to download models from HF Hub (if required)
 2. `HF_HOME`: (optional) to avoid re-downloading models and datasets
 3. `WANDB_API_KEY`: (optional) to enable WandB logging
@@ -125,7 +133,7 @@ Follow the instructions [here](https://github.com/NVIDIA/Megatron-LM/tree/main/e
 __module__: megatron.bridge.recipes.qwen_vl.data.energon.task_encoder
 __class__: ChatMLWebdataset
 field_map:
-  imgs: jpg
+  imgs: jpgs
   conversation: json
 ```
 
diff --git a/src/megatron/bridge/recipes/qwen_vl/data/energon/task_encoder.py b/src/megatron/bridge/recipes/qwen_vl/data/energon/task_encoder.py
index b6301ef5c5..5e8c674ed7 100644
--- a/src/megatron/bridge/recipes/qwen_vl/data/energon/task_encoder.py
+++ b/src/megatron/bridge/recipes/qwen_vl/data/energon/task_encoder.py
@@ -216,17 +216,30 @@ def __init__(self, imagespec):
         self.extensions_mapping = {"jpgs": "jpg", "mp4s": "jpg", "videos": "jpg"}
         self.image_handler = imagehandler(imagespec)
 
+    def _convert_to_tensor(self, data):
+        """Convert numpy array or bytes to tensor.
+        
+        The wds conversion script stores images as numpy arrays (HWC, uint8),
+        so we need to handle both numpy arrays and raw bytes.
+        """
+        if isinstance(data, np.ndarray):
+            # Data is already a numpy array (HWC, uint8) from pickle
+            # Convert to tensor (CHW, float32 in [0,1])
+            return torch.from_numpy(data).permute(2, 0, 1).float() / 255.0
+        else:
+            # Data is raw bytes, use imagehandler to decode
+            return self.image_handler("jpg", data)
+
     def __call__(self, key, data):
         """Perform nested image decoding."""
         extension = re.sub(r".*[.]", "", key)
         if extension.lower() not in self.extensions:
             return None
         data = pickle.loads(data)
-        key = self.extensions_mapping[extension]
         if extension.lower() == "jpgs":
-            data = [self.image_handler(key, d) for d in data]
+            data = [self._convert_to_tensor(d) for d in data]
         else:
-            data = [[self.image_handler(key, d) for d in video] for video in data]
+            data = [[self._convert_to_tensor(d) for d in video] for video in data]
         return data