diff --git a/slime/rollout/data_source.py b/slime/rollout/data_source.py
index fa14c65f0..620f6403f 100644
--- a/slime/rollout/data_source.py
+++ b/slime/rollout/data_source.py
@@ -75,6 +75,7 @@ def __init__(self, args):
                 apply_chat_template=args.apply_chat_template,
                 apply_chat_template_kwargs=args.apply_chat_template_kwargs,
                 seed=args.rollout_seed,
+                data_loading_workers=args.data_loading_workers,
             )
             if self.args.rollout_shuffle:
                 self.dataset.shuffle(self.epoch_id)
diff --git a/slime/rollout/sglang_rollout.py b/slime/rollout/sglang_rollout.py
index a0cc52fb6..151dabb8d 100644
--- a/slime/rollout/sglang_rollout.py
+++ b/slime/rollout/sglang_rollout.py
@@ -467,6 +467,7 @@ async def eval_rollout_single_dataset(
             tool_key=dataset_cfg.tool_key,
             apply_chat_template=args.apply_chat_template,
             apply_chat_template_kwargs=args.apply_chat_template_kwargs,
+            data_loading_workers=args.data_loading_workers,
         )
     dataset = EVAL_PROMPT_DATASET[cache_key]
 
diff --git a/slime/utils/arguments.py b/slime/utils/arguments.py
index 7020fb88d..5dc5c7129 100644
--- a/slime/utils/arguments.py
+++ b/slime/utils/arguments.py
@@ -534,6 +534,12 @@ def add_data_arguments(parser):
                     "When need to add tools during apply_chat_template, you should provide the key for the tools in the prompt dataset."
                 ),
             )
+            parser.add_argument(
+                "--data-loading-workers",
+                type=int,
+                default=1,
+                help="Number of parallel workers for data loading. Higher values speed up VLM data loading.",
+            )
 
             parser.add_argument(
                 "--start-rollout-id",
diff --git a/slime/utils/data.py b/slime/utils/data.py
index 0b9d6a6db..21d68bd50 100644
--- a/slime/utils/data.py
+++ b/slime/utils/data.py
@@ -4,9 +4,11 @@
 import os
 import random
 import re
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 import numpy as np
 import ray
+from tqdm import tqdm
 
 try:
     import pyarrow.parquet as pq
@@ -181,9 +183,14 @@ def __init__(
         seed=42,
         apply_chat_template=False,
         apply_chat_template_kwargs=None,
+        data_loading_workers=1,
     ):
-        origin_samples = []
-        for data in read_file(path):
+        # Phase 1: Read raw data (sequential, fast)
+        raw_data_list = list(read_file(path))
+        logger.info(f"Read {len(raw_data_list)} raw samples from {path}")
+
+        # Define per-sample processing function
+        def process_single_sample(data):
             # Both chat templates and multimodal inputs require conversation format (list of message dicts)
             as_conversation = apply_chat_template or (multimodal_keys is not None)
             prompt = _build_messages(data, prompt_key, as_conversation, multimodal_keys)
@@ -220,15 +227,28 @@ def __init__(
             else:
                 multimodal_inputs = None
 
-            origin_samples.append(
-                Sample(
-                    prompt=output_prompt,
-                    label=data[label_key] if label_key is not None else None,
-                    metadata=metadata,
-                    multimodal_inputs=multimodal_inputs,
-                )
+            return Sample(
+                prompt=output_prompt,
+                label=data[label_key] if label_key is not None else None,
+                metadata=metadata,
+                multimodal_inputs=multimodal_inputs,
             )
 
+        # Phase 2: Process samples in parallel (default to 1 worker if not specified)
+        logger.info(f"Loading data with {data_loading_workers} workers...")
+        origin_samples = [None] * len(raw_data_list)
+        with ThreadPoolExecutor(max_workers=data_loading_workers) as executor:
+            future_to_idx = {
+                executor.submit(process_single_sample, data): idx for idx, data in enumerate(raw_data_list)
+            }
+            for future in tqdm(as_completed(future_to_idx), total=len(raw_data_list), desc="Loading data"):
+                idx = future_to_idx[future]
+                try:
+                    origin_samples[idx] = future.result()
+                except Exception as e:
+                    logger.error(f"Error processing sample {idx}: {e}")
+                    raise
+
         if max_length is not None:
             self.origin_samples = filter_long_prompt(origin_samples, tokenizer, processor, max_length)
         else: