diff --git a/examples/simple_trainer.py b/examples/simple_trainer.py
index ca9271e81..9a994d2c2 100644
--- a/examples/simple_trainer.py
+++ b/examples/simple_trainer.py
@@ -352,7 +352,9 @@ def __init__(
                 scene_scale=self.scene_scale
             )
         elif isinstance(self.cfg.strategy, MCMCStrategy):
-            self.strategy_state = self.cfg.strategy.initialize_state()
+            self.strategy_state = self.cfg.strategy.initialize_state(
+                scene_scale=self.scene_scale
+            )
         else:
             assert_never(self.cfg.strategy)
 
diff --git a/gsplat/strategy/mcmc.py b/gsplat/strategy/mcmc.py
index c07e17376..4b0c3679d 100644
--- a/gsplat/strategy/mcmc.py
+++ b/gsplat/strategy/mcmc.py
@@ -28,6 +28,8 @@ class MCMCStrategy(Strategy):
         refine_stop_iter (int): Stop refining GSs after this iteration. Default to 25_000.
         refine_every (int): Refine GSs every this steps. Default to 100.
         min_opacity (float): GSs with opacity below this value will be pruned. Default to 0.005.
+        prune_scale3d (float): GSs with 3d scale (normalized by scene_scale) above this
+          value will be pruned. Default is 0.1.
         verbose (bool): Whether to print verbose information. Default to False.
 
     Examples:
@@ -37,7 +39,7 @@ class MCMCStrategy(Strategy):
         >>> optimizers: Dict[str, torch.optim.Optimizer] = ...
         >>> strategy = MCMCStrategy()
         >>> strategy.check_sanity(params, optimizers)
-        >>> strategy_state = strategy.initialize_state()
+        >>> strategy_state = strategy.initialize_state(scene_scale=scene_scale)
         >>> for step in range(1000):
         ...     render_image, render_alpha, info = rasterization(...)
         ...     loss = ...
@@ -52,16 +54,17 @@ class MCMCStrategy(Strategy):
     refine_stop_iter: int = 25_000
     refine_every: int = 100
     min_opacity: float = 0.005
+    prune_scale3d: float = 0.1
     verbose: bool = False
 
-    def initialize_state(self) -> Dict[str, Any]:
+    def initialize_state(self, scene_scale: float = 1.0) -> Dict[str, Any]:
         """Initialize and return the running state for this strategy."""
         n_max = 51
         binoms = torch.zeros((n_max, n_max))
         for n in range(n_max):
             for k in range(n + 1):
                 binoms[n, k] = math.comb(n, k)
-        return {"binoms": binoms}
+        return {"binoms": binoms, "scene_scale": scene_scale}
 
     def check_sanity(
         self,
@@ -125,7 +128,7 @@ def step_post_backward(
             and step % self.refine_every == 0
         ):
             # teleport GSs
-            n_relocated_gs = self._relocate_gs(params, optimizers, binoms)
+            n_relocated_gs = self._relocate_gs(params, optimizers, binoms, state)
             if self.verbose:
                 print(f"Step {step}: Relocated {n_relocated_gs} GSs.")
 
@@ -150,9 +153,15 @@ def _relocate_gs(
         params: Union[Dict[str, torch.nn.Parameter], torch.nn.ParameterDict],
         optimizers: Dict[str, torch.optim.Optimizer],
         binoms: Tensor,
+        state: Dict[str, Any],
     ) -> int:
         opacities = torch.sigmoid(params["opacities"].flatten())
         dead_mask = opacities <= self.min_opacity
+        is_too_big = (
+            torch.exp(params["scales"]).max(dim=-1).values
+            > self.prune_scale3d * state["scene_scale"]
+        )
+        dead_mask = dead_mask | is_too_big
         n_gs = dead_mask.sum().item()
         if n_gs > 0:
             relocate(