diff --git a/docs/nerfology/methods/splat.md b/docs/nerfology/methods/splat.md
index 69303f357..fc1bb32a9 100644
--- a/docs/nerfology/methods/splat.md
+++ b/docs/nerfology/methods/splat.md
@@ -45,7 +45,7 @@ A full evalaution of Nerfstudio's implementation of Gaussian Splatting against t
 The default settings provided maintain a balance between speed, quality, and splat file size, but if you care more about quality than training speed or size, you can decrease the alpha cull threshold 
 (threshold to delete translucent gaussians) and disable culling after 15k steps like so: `ns-train splatfacto --pipeline.model.cull_alpha_thresh=0.005 --pipeline.model.continue_cull_post_densification=False --data <data>`
 
-A common artifact in splatting is long, spikey gaussians. [PhysGaussian](https://xpandora.github.io/PhysGaussian/) proposes a scale regularizer that encourages gaussians to be more evenly shaped. To enable this, set the `use_scale_regularization` flag to `True`.
+A common artifact in splatting is long, spikey gaussians. [PhysGaussian](https://xpandora.github.io/PhysGaussian/) proposes a scale regularizer that encourages gaussians to be more evenly shaped. To enable this, set the `pipeline.model.use_scale_regularization` flag to `True`.
 
 ### Details
 For more details on the method, see the [original paper](https://arxiv.org/abs/2308.04079). Additionally, for a detailed derivation of the gradients used in the gsplat library, see [here](https://arxiv.org/abs/2312.02121).