diff --git a/didactic/models/layers.py b/didactic/models/layers.py
index 91bedd19..68c56210 100644
--- a/didactic/models/layers.py
+++ b/didactic/models/layers.py
@@ -77,9 +77,9 @@ def __init__(
         self,
         in_features: int,
         out_features: int,
-        backbone_distribution: Literal["poisson", "binomial"] = "poisson",
+        backbone_distribution: Literal["poisson", "binomial"] = "binomial",
         tau: float = 1.0,
-        tau_mode: Literal["fixed", "learn", "learn_sigm", "learn_fn"] = "learn_sigm",
+        tau_mode: Literal["fixed", "learn", "learn_sigm", "learn_fn"] = "learn_fn",
         eps: float = 1e-6,
     ):
         """Initializes class instance.
@@ -148,13 +148,13 @@ def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
             - (N, 1), Temperature parameter tau, in the range [0, inf) or [0, 1], depending on `tau_mode`.
         """
         # Forward through the linear layer to get a scalar param in [0,1] for the backbone distribution
-        param = self.param_head(x)
-        f_x = (self.num_logits + 1) * param  # Rescale the parameter to [0, num_logits+1]
+        f_x = param = self.param_head(x)
 
         # Compute the probability mass function (PMF) of the backbone distribution
         # For technical reasons, use the log instead of the direct value
         match self.backbone_distribution:
             case "poisson":
+                f_x = (self.num_logits + 1) * f_x  # Rescale f(x) to [0, num_logits+1]
                 log_f = (self.logits_idx * torch.log(f_x + self.eps)) - f_x - torch.log(self.logits_factorial)
             case "binomial":
                 log_f = (