diff --git a/mqboost/objective.py b/mqboost/objective.py
index 2dfb8ab..1c8d070 100644
--- a/mqboost/objective.py
+++ b/mqboost/objective.py
@@ -20,7 +20,7 @@ def _rho(error: np.ndarray, alpha: float) -> np.ndarray:
     return -error * _grad_rho(error=error, alpha=alpha)
 
 
-def _hess_rho(error: np.ndarray, alpha: float) -> np.ndarray:
+def _hess_rho(error: np.ndarray, **kwargs) -> np.ndarray:
     """Compute the Hessian of the check."""
     return np.ones_like(error)
 
@@ -36,7 +36,7 @@ def _grad_huber(error: np.ndarray, alpha: float, delta: float) -> np.ndarray:
     return _r * _smaller_delta + _grad * _bigger_delta
 
 
-def _hess_huber(error: np.ndarray, alpha: float, delta: float) -> np.ndarray:
+def _hess_huber(error: np.ndarray, **kwargs) -> np.ndarray:
     """Compute the Hessian of the huber loss function."""
     return np.ones_like(error)
 
@@ -48,7 +48,7 @@ def _grad_approx(error: np.ndarray, alpha: float, epsilon: float):
     return _grad
 
 
-def _hess_approx(error: np.ndarray, alpha: float, epsilon: float):
+def _hess_approx(error: np.ndarray, epsilon: float, **kwargs):
     """Compute the Hessian of the approx of the smooth approximated check loss function."""
     _hess = 1 / (2 * (epsilon + np.abs(error)))
     return _hess
@@ -70,7 +70,7 @@ def _compute_grads_hess(
     alphas: list[float],
     grad_fn: Callable[[np.ndarray, float, Any], np.ndarray],
     hess_fn: Callable[[np.ndarray, float, Any], np.ndarray],
-    **kwargs: Any,
+    **kwargs: dict[str, float],
 ) -> tuple[np.ndarray, np.ndarray]:
     """Compute gradients and hessians for the given loss function."""
     _len_alpha = len(alphas)