diff --git a/examples/benchmarks/mcmc_deblur.sh b/examples/benchmarks/mcmc_deblur.sh
index fd2ea9b6..b9f87c12 100644
--- a/examples/benchmarks/mcmc_deblur.sh
+++ b/examples/benchmarks/mcmc_deblur.sh
@@ -5,7 +5,7 @@ DATA_FACTOR=4
 RENDER_TRAJ_PATH="spiral"
 CAP_MAX=250000
 
-RESULT_DIR="results/benchmark_mcmc_deblur"
+RESULT_DIR="results/benchmark_mcmc_deblur_wd/1e-3_0.8"
 for SCENE in $SCENE_LIST;
 do
     echo "Running $SCENE"
@@ -14,10 +14,12 @@ do
     CUDA_VISIBLE_DEVICES=0 python simple_trainer.py mcmc --disable_viewer --data_factor $DATA_FACTOR \
         --strategy.cap-max $CAP_MAX \
         --blur_opt \
+        --blur_opt_lr 1e-3 \
+        --blur_a 0.8 \
+        --blur_mask_reg 0.002 \
         --render_traj_path $RENDER_TRAJ_PATH \
         --data_dir $SCENE_DIR/$SCENE/ \
         --result_dir $RESULT_DIR/$SCENE
 done
-
 # Summarize the stats
 python benchmarks/compression/summarize_stats.py --results_dir $RESULT_DIR --scenes $SCENE_LIST --stage val
diff --git a/examples/blur_opt.py b/examples/blur_opt.py
index fed26000..e02a5f5f 100644
--- a/examples/blur_opt.py
+++ b/examples/blur_opt.py
@@ -9,8 +9,9 @@
 class BlurOptModule(nn.Module):
     """Blur optimization module."""
 
-    def __init__(self, n: int, embed_dim: int = 4):
+    def __init__(self, cfg, n: int, embed_dim: int = 4):
         super().__init__()
+        self.blur_a = cfg.blur_a
         self.embeds = torch.nn.Embedding(n, embed_dim)
         self.means_encoder = get_encoder(3, 3)
         self.depths_encoder = get_encoder(3, 1)
@@ -39,9 +40,7 @@ def forward(
         quats: Tensor,
     ):
         quats = F.normalize(quats, dim=-1)
-        means_log = log_transform(means)
-
-        means_emb = self.means_encoder.encode(means_log)
+        means_emb = self.means_encoder.encode(log_transform(means))
         images_emb = self.embeds(image_ids).repeat(means.shape[0], 1)
         mlp_out = self.blur_deltas_mlp(
             torch.cat([images_emb, means_emb, scales, quats], dim=-1)
@@ -61,7 +60,6 @@ def predict_mask(self, image_ids: Tensor, depths: Tensor):
         )
         grid_xy = torch.stack([grid_x, grid_y], dim=-1).unsqueeze(0)
         grid_emb = self.grid_encoder.encode(grid_xy)
-
         depths_emb = self.depths_encoder.encode(log_transform(depths))
         images_emb = self.embeds(image_ids).repeat(*depths_emb.shape[:-1], 1)
         mlp_in = torch.cat([images_emb, grid_emb, depths_emb], dim=-1)
@@ -78,9 +76,7 @@ def mask_loss(self, blur_mask: Tensor, eps: float = 1e-2):
         from collapsing all 0s or 1s. It is biased towards 0 to encourage sparsity.
         """
         x = blur_mask.mean()
-        a = 2.0
-        b = 0.1
-        maskloss = a * (1 / (1 - x + eps) - 1) + b * (1 / (x + eps) - 1)
+        maskloss = self.blur_a * (1 / (1 - x + eps) - 1) + 0.2 * (1 / (x + eps) - 1)
         return maskloss
 
 
diff --git a/examples/simple_trainer.py b/examples/simple_trainer.py
index e6b054ce..3f0c93ce 100644
--- a/examples/simple_trainer.py
+++ b/examples/simple_trainer.py
@@ -154,8 +154,9 @@ class Config:
     blur_opt_lr: float = 1e-3
     # Regularization for blur mask
     blur_mask_reg: float = 0.001
-    # Blur start iteration
-    blur_start_iter: int = 2_000
+    # Regularization for blur optimization as weight decay
+    blur_opt_reg: float = 1e-6
+    blur_a: float = 0.8
 
     # Enable bilateral grid. (experimental)
     use_bilateral_grid: bool = False
@@ -414,12 +415,13 @@ def __init__(
 
         self.blur_optimizers = []
         if cfg.blur_opt:
-            self.blur_module = BlurOptModule(len(self.trainset)).to(self.device)
+            self.blur_module = BlurOptModule(cfg, len(self.trainset)).to(self.device)
             self.blur_module.zero_init()
             self.blur_optimizers = [
                 torch.optim.Adam(
                     self.blur_module.parameters(),
                     lr=cfg.blur_opt_lr * math.sqrt(cfg.batch_size),
+                    weight_decay=cfg.blur_opt_reg,
                 ),
             ]
             if world_size > 1:
@@ -653,7 +655,7 @@ def train(self):
             if cfg.random_bkgd:
                 bkgd = torch.rand(1, 3, device=device)
                 colors = colors + bkgd * (1.0 - alphas)
-            if cfg.blur_opt and step >= cfg.blur_start_iter:
+            if cfg.blur_opt:
                 blur_mask = self.blur_module.predict_mask(image_ids, depths)
                 renders_blur, _, _ = self.rasterize_splats(
                     camtoworlds=camtoworlds,
@@ -706,7 +708,7 @@ def train(self):
             if cfg.use_bilateral_grid:
                 tvloss = 10 * total_variation_loss(self.bil_grids.grids)
                 loss += tvloss
-            if cfg.blur_opt and step >= cfg.blur_start_iter:
+            if cfg.blur_opt:
                 loss += cfg.blur_mask_reg * self.blur_module.mask_loss(blur_mask)
 
             # regularizations