diff --git a/lerobot/common/policies/tdmpc2/configuration_tdmpc2.py b/lerobot/common/policies/tdmpc2/configuration_tdmpc2.py
index 8037875ff..946f70e3d 100644
--- a/lerobot/common/policies/tdmpc2/configuration_tdmpc2.py
+++ b/lerobot/common/policies/tdmpc2/configuration_tdmpc2.py
@@ -70,13 +70,9 @@ class TDMPC2Config:
             be non-zero.
         n_pi_samples: Number of samples to draw from the policy / world model rollout every CEM iteration. Can
             be zero.
-        uncertainty_regularizer_coeff: Coefficient for the uncertainty regularization used when estimating
-            trajectory values (this is the λ coeffiecient in eqn 4 of FOWM).
         n_elites: The number of elite samples to use for updating the gaussian parameters every CEM iteration.
         elite_weighting_temperature: The temperature to use for softmax weighting (by trajectory value) of the
             elites, when updating the gaussian parameters for CEM.
-        gaussian_mean_momentum: Momentum (α) used for EMA updates of the mean parameter μ of the gaussian
-            parameters optimized in CEM. Updates are calculated as μ⁻ ← αμ⁻ + (1-α)μ.
         max_random_shift_ratio: Maximum random shift (as a proportion of the image size) to apply to the
             image(s) (in units of pixels) for training-time augmentation. If set to 0, no such augmentation
             is applied. Note that the input images are assumed to be square for this augmentation.