diff --git a/gsplat/strategy/default.py b/gsplat/strategy/default.py
index 0e311fea..30ffae6c 100644
--- a/gsplat/strategy/default.py
+++ b/gsplat/strategy/default.py
@@ -46,7 +46,10 @@ class DefaultStrategy(Strategy):
         refine_start_iter (int): Start refining GSs after this iteration. Default is 500.
         refine_stop_iter (int): Stop refining GSs after this iteration. Default is 15_000.
         reset_every (int): Reset opacities every this steps. Default is 3000.
-        refine_every (int): Reine GSs every this steps. Default is 100.
+        refine_every (int): Refine GSs every this steps. Default is 100.
+        pause_refine_after_reset (int): Pause refining GSs until this number of steps after
+          reset, Default is 0 (no pause at all) and one might want to set this number to the
+          number of images in training set.
         absgrad (bool): Use absolute gradients for GS splitting. Default is False.
         revised_opacity (bool): Whether to use revised opacity heuristic from
           arXiv:2404.06109 (experimental). Default is False.
@@ -80,6 +83,7 @@ class DefaultStrategy(Strategy):
     refine_stop_iter: int = 15_000
     reset_every: int = 3000
     refine_every: int = 100
+    pause_refine_after_reset: int = 0
     absgrad: bool = False
     revised_opacity: bool = False
     verbose: bool = False
@@ -155,7 +159,11 @@ def step_post_backward(
 
         self._update_state(params, state, info, packed=packed)
 
-        if step > self.refine_start_iter and step % self.refine_every == 0:
+        if (
+            step > self.refine_start_iter
+            and step % self.refine_every == 0
+            and step % self.reset_every >= self.pause_refine_after_reset
+        ):
             # grow GSs
             n_dupli, n_split = self._grow_gs(params, optimizers, state, step)
             if self.verbose:
@@ -175,6 +183,8 @@ def step_post_backward(
             # reset running stats
             state["grad2d"].zero_()
             state["count"].zero_()
+            if self.refine_scale2d_stop_iter > 0:
+                state["radii"].zero_()
             torch.cuda.empty_cache()
 
         if step % self.reset_every == 0:
@@ -258,9 +268,9 @@ def _grow_gs(
         n_dupli = is_dupli.sum().item()
 
         is_large = ~is_small
-        if step < self.refine_scale2d_stop_iter:
-            is_large |= state["radii"] > self.grow_scale2d
         is_split = is_grad_high & is_large
+        if step < self.refine_scale2d_stop_iter:
+            is_split |= state["radii"] > self.grow_scale2d
         n_split = is_split.sum().item()
 
         # first duplicate