diff --git a/CHANGELOG.md b/CHANGELOG.md
index f2daafd0..1a8b4cba 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -12,6 +12,11 @@ changelog does not include internal changes that do not affect the user.
 
 ### Added
 
+- Added `DWA` (Dynamic Weight Average) from [End-to-End Multi-Task Learning with
+  Attention](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_End-To-End_Multi-Task_Learning_With_Attention_CVPR_2019_paper.pdf)
+  (CVPR 2019), a stateful `Scalarizer` that weights each value by the relative rate at which its
+  loss decreased over the two previous epochs. It has no learnable parameters; call its `step()`
+  method once per epoch to roll the loss history.
 - Added `SDMGradWeighting` from [Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms](https://arxiv.org/pdf/2305.18409) (NeurIPS 2023). It is a stateful `Weighting` that solves for task weights via a simplex-projected inner loop on a cross-batch matrix `A = J_1 @ J_2.T` (computed from two independent mini-batches using `autojac.jac`), with a direction-oriented regularizer pulling the descent direction toward a preference direction.
 - Added `IMTL-L` (the loss-balancing variant of Impartial Multi-Task Learning) from [Towards
   Impartial Multi-Task Learning](https://openreview.net/pdf?id=IMPnRXEWpvr) (ICLR 2021), a stateful
diff --git a/docs/source/docs/scalarization/dwa.rst b/docs/source/docs/scalarization/dwa.rst
new file mode 100644
index 00000000..49965ac6
--- /dev/null
+++ b/docs/source/docs/scalarization/dwa.rst
@@ -0,0 +1,7 @@
+:hide-toc:
+
+DWA
+===
+
+.. autoclass:: torchjd.scalarization.DWA
+    :members: __call__, step, reset
diff --git a/docs/source/docs/scalarization/index.rst b/docs/source/docs/scalarization/index.rst
index f2d42be7..fff5d797 100644
--- a/docs/source/docs/scalarization/index.rst
+++ b/docs/source/docs/scalarization/index.rst
@@ -15,6 +15,7 @@ Abstract base class
     :maxdepth: 1
 
     constant.rst
+    dwa.rst
     geometric_mean.rst
     imtl_l.rst
     mean.rst
diff --git a/src/torchjd/scalarization/__init__.py b/src/torchjd/scalarization/__init__.py
index 6b37d281..6f596edd 100644
--- a/src/torchjd/scalarization/__init__.py
+++ b/src/torchjd/scalarization/__init__.py
@@ -20,6 +20,7 @@
 """
 
 from ._constant import Constant
+from ._dwa import DWA
 from ._geometric_mean import GeometricMean
 from ._imtl_l import IMTLL
 from ._mean import Mean
@@ -31,6 +32,7 @@
 
 __all__ = [
     "Constant",
+    "DWA",
     "GeometricMean",
     "IMTLL",
     "Mean",
diff --git a/src/torchjd/scalarization/_dwa.py b/src/torchjd/scalarization/_dwa.py
new file mode 100644
index 00000000..d6d3559f
--- /dev/null
+++ b/src/torchjd/scalarization/_dwa.py
@@ -0,0 +1,137 @@
+import torch
+from torch import Tensor
+from torch.nn.functional import softmax
+
+from torchjd._mixins import Stateful
+
+from ._scalarizer_base import Scalarizer
+
+
+class DWA(Scalarizer, Stateful):
+    r"""
+    :class:`~torchjd.Stateful`
+    :class:`~torchjd.scalarization.Scalarizer` that combines the input tensor of values using Dynamic
+    Weight Average (DWA), proposed in `End-to-End Multi-Task Learning with Attention
+    <https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_End-To-End_Multi-Task_Learning_With_Attention_CVPR_2019_paper.pdf>`_.
+
+    DWA weights each value by how quickly its loss has been decreasing relative to the others. At
+    epoch :math:`t`, the current batch's values are combined as
+
+    .. math::
+        \sum_k \lambda_k(t)\, \ell_k, \qquad
+        \lambda_k(t) = \frac{K \exp(w_k(t-1) / T)}{\sum_i \exp(w_i(t-1) / T)}, \qquad
+        w_k(t-1) = \frac{L_k(t-1)}{L_k(t-2)}
+
+    where:
+
+    - :math:`\ell_k` is the :math:`k`-th value being scalarized (typically the current batch's loss
+      for task k);
+    - :math:`L_k(t)` is the :math:`k`-th value averaged over epoch :math:`t` (used only for the
+      weights);
+    - :math:`w_k(t-1)` is the relative descending rate: the ratio of average losses over the two
+      previous epochs;
+    - :math:`T` is the temperature; a larger :math:`T` makes the weights more uniform;
+    - :math:`K` is the number of values (e.g. the number of tasks); the factor :math:`K` keeps
+      :math:`\sum_k \lambda_k = K`.
+
+    The weights use only the two previous epochs' average losses, so they need no gradient. At each
+    call, the scalarization is returned and the current batch's losses are summed to the current
+    epoch's loss sums. :meth:`step` must then be called once at the end of each epoch to finalize
+    that epoch's average loss and roll the history forward. During the first two epochs, before two
+    averages are available, the weights are uniform.
+
+    :param temperature: The temperature :math:`T`. Must be strictly positive. Larger values make the
+        weights more uniform. The paper uses ``2.0``.
+
+    The following example shows how to train a model with DWA. The scalarizer is called on every
+    batch, and :meth:`step` is called once at the end of each epoch.
+
+        >>> import torch
+        >>> from torch.nn import Linear
+        >>>
+        >>> from torchjd.scalarization import DWA
+        >>>
+        >>> model = Linear(3, 2)
+        >>> scalarizer = DWA()
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+        >>>
+        >>> for epoch in range(3):
+        ...     for _ in range(4):  # Iterate over the batches of the epoch.
+        ...         features = torch.randn(8, 3)
+        ...         losses = model(features).pow(2).mean(dim=0)  # One loss per output dimension.
+        ...         loss = scalarizer(losses)
+        ...         optimizer.zero_grad()
+        ...         loss.backward()
+        ...         optimizer.step()
+        ...     scalarizer.step()  # Roll the epoch history once, at the end of the epoch.
+
+    .. note::
+        DWA weights each value by the ratio of its losses over consecutive epochs, which the paper
+        defines as a descending rate in the range :math:`(0, +\infty)`. The losses are therefore
+        expected to keep a consistent, nonzero sign across epochs (they need not be positive).
+    """
+
+    def __init__(self, temperature: float = 2.0) -> None:
+        if temperature <= 0.0:
+            raise ValueError(
+                f"Parameter `temperature` should be strictly positive. Found `temperature = "
+                f"{temperature}`."
+            )
+
+        super().__init__()
+        self.temperature = temperature
+        self._loss_sum: Tensor | None = None
+        self._n_batches: int = 0
+        self._previous_averages: list[Tensor] = []
+
+    def forward(self, values: Tensor, /) -> Tensor:
+        weights = self._compute_weights(values)
+
+        detached = values.detach()
+        if self._loss_sum is None:
+            self._loss_sum = detached.clone()
+        elif self._loss_sum.shape != detached.shape:
+            raise ValueError(
+                f"The shape of `values` changed from {tuple(self._loss_sum.shape)} to "
+                f"{tuple(detached.shape)} within an epoch. Call `reset()` before changing it."
+            )
+        else:
+            self._loss_sum = self._loss_sum + detached
+        self._n_batches += 1
+
+        return (weights * values).sum()
+
+    def step(self) -> None:
+        """
+        Finalizes the current epoch's average loss and rolls the history forward, discarding the
+        average from two epochs ago. Should be called once at the end of each epoch.
+        """
+
+        if self._loss_sum is None:
+            return
+        average = self._loss_sum / self._n_batches
+        self._previous_averages = [*self._previous_averages, average][-2:]
+        self._loss_sum = None
+        self._n_batches = 0
+
+    def reset(self) -> None:
+        self._loss_sum = None
+        self._n_batches = 0
+        self._previous_averages = []
+
+    def _compute_weights(self, values: Tensor) -> Tensor:
+        if len(self._previous_averages) < 2:
+            return torch.ones_like(values)
+        older = self._previous_averages[0]
+        newer = self._previous_averages[1]
+        if older.shape != values.shape:
+            raise ValueError(
+                f"The shape of `values` changed from {tuple(older.shape)} to "
+                f"{tuple(values.shape)}. Call `reset()` before changing it."
+            )
+        rates = (newer / older).flatten()
+        weights = softmax(rates / self.temperature, dim=0)
+        return values.numel() * weights.reshape(values.shape)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(temperature={self.temperature})"
diff --git a/tests/unit/scalarization/test_dwa.py b/tests/unit/scalarization/test_dwa.py
new file mode 100644
index 00000000..d38fe0c1
--- /dev/null
+++ b/tests/unit/scalarization/test_dwa.py
@@ -0,0 +1,153 @@
+import torch
+from pytest import mark, raises
+from torch import Tensor
+from utils.tensors import ones_, tensor_
+
+from torchjd.scalarization import DWA
+
+from ._asserts import assert_grad_flow, assert_returns_scalar
+from ._inputs import all_inputs
+
+
+def test_uniform_weights_for_first_two_epochs() -> None:
+    dwa = DWA(temperature=2.0)
+    # Epoch 1: no completed epoch yet, so weights are uniform (sum).
+    torch.testing.assert_close(dwa(tensor_([1.0, 3.0])), tensor_(4.0))
+    dwa.step()
+    # Epoch 2: only one completed epoch, so weights are still uniform (sum).
+    torch.testing.assert_close(dwa(tensor_([2.0, 5.0])), tensor_(7.0))
+    dwa.step()
+
+
+def test_weights_from_previous_two_epochs() -> None:
+    dwa = DWA(temperature=2.0)
+    dwa(tensor_([1.0, 1.0]))
+    dwa.step()  # Epoch 1 average = [1, 1].
+    dwa(tensor_([1.0, 4.0]))
+    dwa.step()  # Epoch 2 average = [1, 4].
+    # Epoch 3: rates = [1, 4] / [1, 1] = [1, 4].
+    losses = tensor_([3.0, 5.0])
+    result = dwa(losses)
+    expected_weights = 2.0 * torch.softmax(tensor_([1.0, 4.0]) / 2.0, dim=0)
+    torch.testing.assert_close(result, (expected_weights * losses).sum())
+
+
+def test_uses_per_epoch_average() -> None:
+    # The weights use the average loss over each epoch's batches, not just the last batch.
+    dwa = DWA(temperature=2.0)
+    dwa(tensor_([2.0, 2.0]))
+    dwa(tensor_([0.0, 0.0]))
+    dwa.step()  # Epoch 1 average = [1, 1].
+    dwa(tensor_([2.0, 6.0]))
+    dwa(tensor_([0.0, 2.0]))
+    dwa.step()  # Epoch 2 average = [1, 4].
+    losses = tensor_([3.0, 5.0])
+    result = dwa(losses)
+    expected_weights = 2.0 * torch.softmax(tensor_([1.0, 4.0]) / 2.0, dim=0)
+    torch.testing.assert_close(result, (expected_weights * losses).sum())
+
+
+def test_step_discards_oldest_epoch() -> None:
+    dwa = DWA(temperature=2.0)
+    dwa(tensor_([9.0, 9.0]))
+    dwa.step()  # Epoch 1 average = [9, 9]; should be discarded after epoch 3.
+    dwa(tensor_([1.0, 1.0]))
+    dwa.step()  # Epoch 2 average = [1, 1].
+    dwa(tensor_([1.0, 4.0]))
+    dwa.step()  # Epoch 3 average = [1, 4].
+    # Epoch 4 uses only epochs 2 and 3: rates = [1, 4] / [1, 1] = [1, 4].
+    losses = tensor_([3.0, 5.0])
+    result = dwa(losses)
+    expected_weights = 2.0 * torch.softmax(tensor_([1.0, 4.0]) / 2.0, dim=0)
+    torch.testing.assert_close(result, (expected_weights * losses).sum())
+
+
+def test_weights_sum_to_numel() -> None:
+    dwa = DWA()
+    dwa(tensor_([1.0, 2.0]))
+    dwa.step()
+    dwa(tensor_([2.0, 1.0]))
+    dwa.step()
+    # The weights sum to the number of elements, so weighting a vector of ones gives that count.
+    torch.testing.assert_close(dwa(ones_((2,))), tensor_(2.0))
+
+
+@mark.parametrize("values", all_inputs)
+def test_expected_structure(values: Tensor) -> None:
+    assert_returns_scalar(DWA(), values)
+
+
+@mark.parametrize("values", all_inputs)
+def test_grad_flow(values: Tensor) -> None:
+    assert_grad_flow(DWA(), values)
+
+
+def test_grad_flows_with_computed_weights() -> None:
+    # After two epochs the weights are computed from the (detached) loss history; gradients must
+    # still flow to the current values.
+    dwa = DWA(temperature=2.0)
+    dwa(tensor_([1.0, 1.0]))
+    dwa.step()
+    dwa(tensor_([1.0, 4.0]))
+    dwa.step()
+    assert_grad_flow(dwa, tensor_([3.0, 5.0]))
+
+
+def test_reset() -> None:
+    dwa = DWA()
+    dwa(tensor_([1.0, 2.0]))
+    dwa.step()
+    dwa(tensor_([3.0, 4.0]))
+    dwa.reset()
+    assert dwa._previous_averages == []
+    assert dwa._loss_sum is None
+    assert dwa._n_batches == 0
+
+
+def test_step_without_forward_is_noop() -> None:
+    dwa = DWA()
+    dwa.step()  # No losses accumulated yet.
+    assert dwa._previous_averages == []
+
+
+def test_supports_consistently_negative_losses() -> None:
+    # DWA works on negative losses too, as long as each value keeps a consistent sign: the ratio of
+    # same-sign losses is positive, so the weights match those of the equivalent positive case.
+    dwa = DWA(temperature=2.0)
+    dwa(tensor_([-2.0, -2.0]))
+    dwa.step()  # Epoch 1 average = [-2, -2].
+    dwa(tensor_([-2.0, -8.0]))
+    dwa.step()  # Epoch 2 average = [-2, -8]; rates = [-2, -8] / [-2, -2] = [1, 4].
+    losses = tensor_([3.0, 5.0])
+    result = dwa(losses)
+    expected_weights = 2.0 * torch.softmax(tensor_([1.0, 4.0]) / 2.0, dim=0)
+    torch.testing.assert_close(result, (expected_weights * losses).sum())
+
+
+def test_raises_on_shape_change_within_epoch() -> None:
+    dwa = DWA()
+    dwa(tensor_([1.0, 2.0]))
+    with raises(ValueError):
+        dwa(tensor_([1.0, 2.0, 3.0]))
+
+
+def test_raises_on_shape_change_between_epochs() -> None:
+    dwa = DWA()
+    dwa(tensor_([1.0, 2.0]))
+    dwa.step()
+    dwa(tensor_([2.0, 1.0]))
+    dwa.step()
+    with raises(ValueError):
+        dwa(tensor_([1.0, 2.0, 3.0]))
+
+
+@mark.parametrize("temperature", [0.0, -1.0])
+def test_raises_on_non_positive_temperature(temperature: float) -> None:
+    with raises(ValueError):
+        DWA(temperature=temperature)
+
+
+def test_representations() -> None:
+    assert repr(DWA()) == "DWA(temperature=2.0)"
+    assert repr(DWA(temperature=1.5)) == "DWA(temperature=1.5)"
+    assert str(DWA()) == "DWA"