diff --git a/solidago/src/solidago/preference_learning/generalized_bradley_terry.py b/solidago/src/solidago/preference_learning/generalized_bradley_terry.py
index d737de0495..20c56c768f 100644
--- a/solidago/src/solidago/preference_learning/generalized_bradley_terry.py
+++ b/solidago/src/solidago/preference_learning/generalized_bradley_terry.py
@@ -50,7 +50,7 @@ def cumulant_generating_function_derivative(self) -> Callable[[npt.NDArray], npt
 
     @property
     @abstractmethod
-    def loss_function(self) -> Callable[[npt.NDArray, npt.NDArray, float], float]:
+    def log_likelihood_function(self) -> Callable[[npt.NDArray, npt.NDArray], float]:
         """The loss function definition is used only to compute uncertainties.
         """
 
@@ -59,12 +59,11 @@ def loss_increase_to_solve(self):
         """The root of this function is used to compute asymetric uncertainties
         by looking for the delta for which the loss is increased by 1.
         """
-        loss_function = self.loss_function
+        ll_function = self.log_likelihood_function
 
         @njit
-        def f(delta, theta_diff, r, coord_indicator, loss_actual, norm2_actual, solution_actual):
-            norm2 = norm2_actual - solution_actual**2 + (solution_actual + delta) ** 2
-            return loss_function(theta_diff + delta * coord_indicator, r, norm2) - loss_actual - 1.0
+        def f(delta, theta_diff, r, coord_indicator, ll_actual):
+            return ll_function(theta_diff + delta * coord_indicator, r) - ll_actual - 1.0
 
         return f
 
@@ -143,17 +142,16 @@ def get_derivative_args(coord: int, sol: np.ndarray):
 
         uncertainties_left = np.empty_like(solution)
         uncertainties_right = np.empty_like(solution)
-        norm2_actual = (solution ** 2).sum()
-        loss_actual = self.loss_function(score_diff, r_actual, norm2_actual)
+        ll_actual = self.log_likelihood_function(score_diff, r_actual)
 
-        for coordinate, solution_coord in enumerate(solution):
+        for coordinate in range(len(solution)):
             comparison_indicator = (
                 (comparisons["entity_a_coord"] == coordinate).astype(int)
                 - (comparisons["entity_b_coord"] == coordinate).astype(int)
             ).to_numpy()
             uncertainties_left[coordinate] = -1 * njit_brentq(
                 self.loss_increase_to_solve,
-                args=(score_diff, r_actual, comparison_indicator, loss_actual, norm2_actual, solution_coord),
+                args=(score_diff, r_actual, comparison_indicator, ll_actual),
                 xtol=self.convergence_error,
                 a=-1.0,
                 b=0.0,
@@ -161,7 +159,7 @@ def get_derivative_args(coord: int, sol: np.ndarray):
             )
             uncertainties_right[coordinate] = njit_brentq(
                 self.loss_increase_to_solve,
-                args=(score_diff, r_actual, comparison_indicator, loss_actual, norm2_actual, solution_coord),
+                args=(score_diff, r_actual, comparison_indicator, ll_actual),
                 xtol=self.convergence_error,
                 a=0.0,
                 b=1.0
@@ -256,16 +254,11 @@ def __init__(
         self.cumulant_generating_function_error = cumulant_generating_function_error
 
     @cached_property
-    def loss_function(self):
-        prior_std_dev = self.prior_std_dev
-
+    def log_likelihood_function(self):
         @njit
-        def loss(score_diff, r, norm2):
-            return (np.log(np.sinh(score_diff) / score_diff) + r * score_diff).sum() + norm2 / (
-                2 * prior_std_dev**2
-            )
-
-        return loss
+        def f(score_diff, r):
+            return (np.log(np.sinh(score_diff) / score_diff) + r * score_diff).sum()
+        return f
 
     @cached_property
     def cumulant_generating_function_derivative(self) -> Callable[[npt.NDArray], npt.NDArray]: