diff --git a/timm/optim/kron.py b/timm/optim/kron.py
index 25c1b047..533354ec 100644
--- a/timm/optim/kron.py
+++ b/timm/optim/kron.py
@@ -95,7 +95,9 @@ class Kron(torch.optim.Optimizer):
         precond_dtype: Dtype of the preconditioner.
         decoupled_decay: AdamW style decoupled weight decay
         flatten: Flatten dimensions instead of fully relying on expressions for higher rank params
-        flatten_start_end: Range of dimensions to flatten, defaults to (2, -1).
+        flatten_start_dim: Start of flatten range, defaults to 2. Seems good tradeoff for ConvNets.
+        flatten_end_dim: End of flatten range, defaults to -1.
+        stochastic_weight_decay: Enable random modulation of weight decay
         deterministic: Deterministic behaviour across save / load (resume). FIXME slow, needs work
     """
 
@@ -118,6 +120,7 @@ class Kron(torch.optim.Optimizer):
         flatten: bool = False,
         flatten_start_dim: int = 2,
         flatten_end_dim: int = -1,
+        stochastic_weight_decay: bool = False,
         deterministic: bool = False,
     ):
         if not has_opt_einsum:
@@ -147,6 +150,7 @@ class Kron(torch.optim.Optimizer):
             flatten=flatten,
             flatten_start_dim=flatten_start_dim,
             flatten_end_dim=flatten_end_dim,
+            stochastic_weight_decay=stochastic_weight_decay,
         )
         super(Kron, self).__init__(params, defaults)
 
@@ -353,11 +357,15 @@ class Kron(torch.optim.Optimizer):
                     pre_grad = pre_grad.view(p.shape)
 
                 # Apply weight decay
-                if group["weight_decay"] != 0:
+                weight_decay = group["weight_decay"]
+                if weight_decay != 0:
+                    if group["stochastic_weight_decay"]:
+                        weight_decay = 2 * self.rng.random() * weight_decay
+
                     if group["decoupled_decay"]:
-                        p.mul_(1. - group["lr"] * group["weight_decay"])
+                        p.mul_(1. - group["lr"] * weight_decay)
                     else:
-                        pre_grad.add_(p, alpha=group["weight_decay"])
+                        pre_grad.add_(p, alpha=weight_decay)
 
                 # Update parameters
                 p.add_(pre_grad, alpha=-group["lr"])