diff --git a/simpeg/directives/directives.py b/simpeg/directives/directives.py
index ef554a24c1..1b3a19dcc6 100644
--- a/simpeg/directives/directives.py
+++ b/simpeg/directives/directives.py
@@ -3575,6 +3575,7 @@ class ScaleMisfitMultipliers(InversionDirective):
 
     def __init__(self, path: Path | None, **kwargs):
         self.last_beta = None
+        self.chi_factors = None
 
         if path is None:
             path = Path()
@@ -3599,41 +3600,52 @@ def initialize(self):
 
     def endIter(self):
         ratio = self.invProb.beta / self.last_beta
-
-        if ratio > 1:
-            return
-
         chi_factors = []
         for objfct, pred in zip(self.invProb.dmisfit.objfcts, self.invProb.dpred):
             residual = objfct.W * (objfct.data.dobs - pred)
             phi_d = np.vdot(residual, residual)
             chi_factors.append(phi_d / objfct.nD)
 
-        chi_factors = np.asarray(chi_factors)
+        self.chi_factors = np.asarray(chi_factors)
+
+        if np.all(self.chi_factors < 1) or ratio >= 1:
+            self.last_beta = self.invProb.beta
+            self.write_log()
+            return
 
         # Normalize scaling between [ratio, 1]
         scalings = (
-            1 - (1 - ratio) * (chi_factors.max() - chi_factors) / chi_factors.max()
+            1
+            - (1 - ratio)
+            * (self.chi_factors.max() - self.chi_factors)
+            / self.chi_factors.max()
         )
 
         # Force the ones that overshot target
-        scalings[chi_factors < 1] = ratio * chi_factors[chi_factors < 1]
+        scalings[self.chi_factors < 1] = (
+            ratio  # * self.chi_factors[self.chi_factors < 1]
+        )
 
         # Update the scaling
         self.scalings *= scalings
 
         # Normalize total phi_d with scalings
-        multipliers = self.multipliers * self.scalings
+        self.invProb.dmisfit.multipliers = self.multipliers * self.scalings
+        self.last_beta = self.invProb.beta
+        self.write_log()
 
+    def write_log(self):
+        """
+        Write the scaling factors to the log file.
+        """
         with open(self.filepath, "a", encoding="utf-8") as f:
             f.write(
                 f"{self.opt.iter}\t"
                 + "\t".join(
                     f"{multi:.2e} * {chi:.2e}"
-                    for multi, chi in zip(multipliers, chi_factors)
+                    for multi, chi in zip(
+                        self.invProb.dmisfit.multipliers, self.chi_factors
+                    )
                 )
                 + "\n"
             )
-
-        self.invProb.dmisfit.multipliers = multipliers.tolist()
-        self.last_beta = self.invProb.beta