refactor(aggregation): Register state tensors as buffers

src/torchjd/aggregation/_cr_mogm.py

@@ -105,7 +105,7 @@ def __init__(
         self.weighting = weighting
         self.alpha = alpha
         self._initial_weights = initial_weights
-        self._lambda: Tensor | None = None
+        self.register_buffer("_lambda", None)
 
     @property
     def alpha(self) -> float:

src/torchjd/aggregation/_gradvac.py

@@ -44,7 +44,7 @@ def __init__(self, beta: float = 0.5, eps: float = 1e-8) -> None:
         super().__init__()
         self.beta = beta
         self.eps = eps
-        self._phi_t: Tensor | None = None
+        self.register_buffer("_phi_t", None)
         self._state_key: tuple[int, torch.dtype] | None = None
 
     @property

src/torchjd/aggregation/_modo.py

@@ -133,7 +133,7 @@ def __init__(self, gamma: float = 0.1, rho: float = 0.1) -> None:
         super().__init__()
         self.gamma = gamma
         self.rho = rho
-        self._lambda: Tensor | None = None
+        self.register_buffer("_lambda", None)
         self._state_key: tuple[int, torch.dtype, torch.device] | None = None
 
     @property

src/torchjd/aggregation/_sdmgrad.py

@@ -107,7 +107,7 @@ def __init__(
         self.n_iter = n_iter
         self.lambda_ = lambda_
         self.pref_vector = pref_vector
-        self._w: Tensor | None = None
+        self.register_buffer("_w", None)
         self._state_key: tuple[int, torch.dtype, torch.device] | None = None
 
     @property