diff --git a/sample.py b/sample.py
index 4d5e327..5889163 100644
--- a/sample.py
+++ b/sample.py
@@ -59,6 +59,8 @@ class Args:
     param_dtype = jnp.float32
     dtype = jnp.bfloat16
     use_flash_attention: bool = True
+    # Additional parameters
+    darkness_threshold: float = 0.0
 
 
 args = tyro.cli(Args)
@@ -195,6 +197,7 @@ def _autoreg_sample(genie, rng, video_batch_BSHWC, action_batch_E):
         num_workers=0,
         prefetch_buffer_size=1,
         seed=args.seed,
+        darkness_threshold=args.darkness_threshold,
     )
     dataloader = iter(dataloader)
     video_batch_BSHWC = next(dataloader)
diff --git a/train_dynamics.py b/train_dynamics.py
index c9707f0..fc7257e 100644
--- a/train_dynamics.py
+++ b/train_dynamics.py
@@ -44,6 +44,7 @@ class Args:
     )
     warmup_steps: int = 5000
     lr_schedule: str = "wsd"  # supported options: wsd, cos
+    darkness_threshold: float = 0.0
     # Tokenizer
     tokenizer_dim: int = 512
     tokenizer_ffn_dim: int = 2048
@@ -319,6 +320,7 @@ def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:
         # The dataloader shards the dataset across all processes
         args.batch_size,
         *image_shape,
+        darkness_threshold=args.darkness_threshold,
         num_workers=8,
         prefetch_buffer_size=1,
         seed=args.seed,
diff --git a/train_lam.py b/train_lam.py
index 388f355..834850a 100644
--- a/train_lam.py
+++ b/train_lam.py
@@ -46,6 +46,7 @@ class Args:
     warmup_steps: int = 5000
     lr_schedule: str = "wsd"  # supported options: wsd, cos
     vq_reset_thresh: int = 50
+    darkness_threshold: float = 0.0
     # LAM
     model_dim: int = 512
     ffn_dim: int = 2048
@@ -297,6 +298,7 @@ def loss_fn(
         # The dataloader shards the dataset across all processes
         args.batch_size,
         *image_shape,
+        darkness_threshold=args.darkness_threshold,
         num_workers=8,
         prefetch_buffer_size=1,
         seed=args.seed,
diff --git a/train_tokenizer.py b/train_tokenizer.py
index 5e71b2f..f3c36cb 100644
--- a/train_tokenizer.py
+++ b/train_tokenizer.py
@@ -45,6 +45,7 @@ class Args:
     )
     lr_schedule: str = "wsd"  # supported options: wsd, cos
     warmup_steps: int = 10000
+    darkness_threshold: float = 0.0
     # Tokenizer
     model_dim: int = 512
     ffn_dim: int = 2048
@@ -289,6 +290,7 @@ def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:
         # The dataloader shards the dataset across all processes
         args.batch_size,
         *image_shape,
+        darkness_threshold=args.darkness_threshold,
         num_workers=8,
         prefetch_buffer_size=1,
         seed=args.seed,
diff --git a/utils/dataloader.py b/utils/dataloader.py
index 448e38a..b6dc4ef 100644
--- a/utils/dataloader.py
+++ b/utils/dataloader.py
@@ -4,6 +4,7 @@
 from typing import Any
 import pickle
 
+RGB_TO_GRAYSCALE_WEIGHTS = np.array([0.2989, 0.5870, 0.1140])
 
 class EpisodeLengthFilter(grain.transforms.Filter):
     """
@@ -96,6 +97,38 @@ def random_map(self, element: dict, rng: np.random.Generator) -> Any:
         return seq
 
 
+class DarknessFilter(grain.transforms.Filter):
+    """
+    A Grain Filter that filters out sequences with images that are too dark.
+    """
+
+    def __init__(self, darkness_threshold: float):
+        """Initializes the filter with darkness threshold."""
+        self.darkness_threshold = darkness_threshold
+
+    def filter(self, element: Any) -> bool:
+        """
+        Filters sequences based on darkness.
+
+        Args:
+            element: A NumPy array representing a processed video sequence.
+
+        Returns:
+            True if the sequence is not too dark, False otherwise.
+        """
+        # Convert the RGB image to grayscale using numpy
+        element_greyscale = np.dot(element[...,:3], RGB_TO_GRAYSCALE_WEIGHTS)
+        average_brightness = np.mean(element_greyscale)
+        if average_brightness < self.darkness_threshold:
+            print(
+                f"Filtering out sequence with average brightness {average_brightness}, "
+                f"which is below the darkness threshold {self.darkness_threshold}."
+            )
+            return False
+
+        return True
+
+
 def get_dataloader(
     array_record_paths: list[str],
     seq_len: int,
@@ -103,6 +136,7 @@ def get_dataloader(
     image_h: int,
     image_w: int,
     image_c: int,
+    darkness_threshold: float = 0.,
     num_workers: int = 1,
     prefetch_buffer_size: int = 1,
     seed: int = 42,
@@ -139,6 +173,9 @@ def get_dataloader(
         ProcessEpisodeAndSlice(
             seq_len=seq_len, image_h=image_h, image_w=image_w, image_c=image_c
         ),
+        DarknessFilter(
+            darkness_threshold=darkness_threshold
+        ),
         grain.transforms.Batch(batch_size=per_process_batch_size, drop_remainder=True),
     ]