Simplify the note about GradVac grouping and make it use explicitly the names used in GradVac's paper and in LibMTL

Author: ValerianRey

src/torchjd/aggregation/_gradvac.py

@@ -42,12 +42,8 @@ class GradVac(GramianWeightedAggregator, Stateful):
         you need reproducibility.
 
     .. note::
-        To apply GradVac with per-layer or per-parameter-group granularity, create a separate
-        :class:`GradVac` instance for each group and call
-        :func:`~torchjd.autojac.jac_to_grad` once per group after
-        :func:`~torchjd.autojac.mtl_backward`. Each instance maintains its own EMA state,
-        matching the per-block targets :math:`\hat{\phi}_{ijk}` from the original paper. See
-        the :doc:`Grouping </examples/grouping>` example for details.
+        To apply GradVac with the `whole_model`, `enc_dec`, `all_layer` or `all_matrix` grouping
+        strategy, please refer to the :doc:`Grouping </examples/grouping>` examples.
     """
 
     def __init__(self, beta: float = 0.5, eps: float = 1e-8) -> None: