diff --git a/examples/simple_trainer.py b/examples/simple_trainer.py
index 73978234..f52b886b 100644
--- a/examples/simple_trainer.py
+++ b/examples/simple_trainer.py
@@ -85,6 +85,8 @@ class Config:
     eval_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
     # Steps to save the model
     save_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
+    # Steps to save the model as ply
+    ply_steps: List[int] = field(default_factory=lambda: [7_000, 30_000])
 
     # Initialization strategy
     init_type: str = "sfm"
@@ -165,6 +167,7 @@ class Config:
     def adjust_steps(self, factor: float):
         self.eval_steps = [int(i * factor) for i in self.eval_steps]
         self.save_steps = [int(i * factor) for i in self.save_steps]
+        self.ply_steps = [int(i * factor) for i in self.ply_steps]
         self.max_steps = int(self.max_steps * factor)
         self.sh_degree_interval = int(self.sh_degree_interval * factor)
 
@@ -765,7 +768,8 @@ def train(self):
                 torch.save(
                     data, f"{self.ckpt_dir}/ckpt_{step}_rank{self.world_rank}.pt"
                 )
-                save_ply(self.splats, f"{self.ply_dir}/point_cloud.ply")
+            if step in [i - 1 for i in cfg.ply_steps] or step == max_steps - 1:
+                save_ply(self.splats, f"{self.ply_dir}/point_cloud_{step}.ply")
 
             if isinstance(self.cfg.strategy, DefaultStrategy):
                 self.cfg.strategy.step_post_backward(