diff --git a/README.md b/README.md
index 22a4bd25..8eec62bc 100644
--- a/README.md
+++ b/README.md
@@ -215,58 +215,7 @@ losses with Jacobian descent using [UPGrad](https://torchjd.org/stable/docs/aggr
 ```
 
 You can even go one step further by considering the multiple tasks and each element of the batch
-independently. We call that Instance-Wise Multitask Learning (IWMTL).
-
-```python
-import torch
-from torch.nn import Linear, MSELoss, ReLU, Sequential
-from torch.optim import SGD
-
-from torchjd.aggregation import Flattening, UPGradWeighting
-from torchjd.autogram import Engine
-
-shared_module = Sequential(Linear(10, 5), ReLU(), Linear(5, 3), ReLU())
-task1_module = Linear(3, 1)
-task2_module = Linear(3, 1)
-params = [
-    *shared_module.parameters(),
-    *task1_module.parameters(),
-    *task2_module.parameters(),
-]
-
-optimizer = SGD(params, lr=0.1)
-mse = MSELoss(reduction="none")
-weighting = Flattening(UPGradWeighting())
-engine = Engine(shared_module, batch_dim=0)
-
-inputs = torch.randn(8, 16, 10)  # 8 batches of 16 random input vectors of length 10
-task1_targets = torch.randn(8, 16)  # 8 batches of 16 targets for the first task
-task2_targets = torch.randn(8, 16)  # 8 batches of 16 targets for the second task
-
-for input, target1, target2 in zip(inputs, task1_targets, task2_targets):
-    features = shared_module(input)  # shape: [16, 3]
-    out1 = task1_module(features).squeeze(1)  # shape: [16]
-    out2 = task2_module(features).squeeze(1)  # shape: [16]
-
-    # Compute the matrix of losses: one loss per element of the batch and per task
-    losses = torch.stack([mse(out1, target1), mse(out2, target2)], dim=1)  # shape: [16, 2]
-
-    # Compute the gramian (inner products between pairs of gradients of the losses)
-    gramian = engine.compute_gramian(losses)  # shape: [16, 2, 2, 16]
-
-    # Obtain the weights that lead to no conflict between reweighted gradients
-    weights = weighting(gramian)  # shape: [16, 2]
-
-    # Do the standard backward pass, but weighted using the obtained weights
-    losses.backward(weights)
-    optimizer.step()
-    optimizer.zero_grad()
-```
-
-> [!NOTE]
-> Here, because the losses are a matrix instead of a simple vector, we compute a *generalized
-> Gramian* and we extract weights from it using a
-> [GeneralizedWeighting](https://torchjd.org/stable/docs/aggregation/#torchjd.aggregation.GeneralizedWeighting).
+independently (Instance-Wise Multitask Learning). See [this example](https://torchjd.org/stable/examples/iwmtl/) for more details.
 
 More usage examples can be found [here](https://torchjd.org/stable/examples/).