feat(scalarization): Add readme

src/torchjd/scalarization/README.md

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+# Scalarization
+
+A `Scalarizer` reduces a tensor of values (typically a vector of per-task or per-instance losses)
+into a single scalar that can be optimized with a standard `loss.backward()`. Scalarizers are the
+simple baseline against which the Jacobian-descent [aggregators](../aggregation) are compared:
+instead of combining the per-loss gradients, a scalarizer combines the losses directly.
+
+Full documentation for every scalarizer is at
+[torchjd.org](https://torchjd.org/latest/docs/scalarization/).
+
+## Usage
+
+```python
+import torch
+from torch.nn import Linear
+from torchjd.scalarization import Mean
+
+model = Linear(3, 2)
+scalarizer = Mean()
+
+features = torch.randn(8, 3)
+losses = model(features).pow(2).mean(dim=0)  # one loss per output dimension
+loss = scalarizer(losses)
+loss.backward()  # gradients flow to the model parameters
+```
+
+## Available scalarizers
+
+- **Constant**: combines the values with constant, pre-determined weights.
+- **COSMOS**: linear scalarization minus a cosine-similarity penalty toward a preference direction.
+- **DWA**: weights each value by the relative rate at which its loss decreased over the two previous
+  epochs.
+- **FAMO**: decreases all task losses at an approximately equal rate, learning the task weights
+  internally.
+- **GeometricMean**: geometric mean of the values (also known as GLS).
+- **IMTLL**: learns a per-task scale and combines the values as the sum of `exp(s_i) * L_i - s_i`.
+- **Mean**: mean of the values.
+- **PBI**: decomposes the values along a preference direction and penalizes the perpendicular
+  distance.
+- **Random**: combines the values with positive random weights summing to one.
+- **STCH**: smooth approximation of the weighted, shifted maximum of the values.
+- **Sum**: sum of the values.
+- **UW**: weights the values using learned per-task uncertainties.
+
+`UW`, `IMTLL`, and `FAMO` are trainable, and `DWA` and `FAMO` carry state between calls, so they
+need a little more than a single call (an optimizer, a per-epoch `step()`, or an `update()`). See
+the documentation for the exact usage.