diff --git a/.jenkins/validate_tutorials_built.py b/.jenkins/validate_tutorials_built.py
index 6dc96f0517..8d19cee75c 100644
--- a/.jenkins/validate_tutorials_built.py
+++ b/.jenkins/validate_tutorials_built.py
@@ -42,7 +42,6 @@
     "intermediate_source/torchrec_intro_tutorial.py", #failing with 2.8 reenable after 3498
     "intermediate_source/torch_export_tutorial.py",  # failing with 2.11 issue #3773
     "beginner_source/mosaic_memory_profiling_tutorial.py",  # failing with 2.11 issue #3774
-    "intermediate_source/variable_length_attention_tutorial.py",  # failing with 2.11 issue #3775
 ]
 
 def tutorial_source_dirs() -> List[Path]:
diff --git a/intermediate_source/variable_length_attention_tutorial.py b/intermediate_source/variable_length_attention_tutorial.py
index 261695723e..7c452b9546 100644
--- a/intermediate_source/variable_length_attention_tutorial.py
+++ b/intermediate_source/variable_length_attention_tutorial.py
@@ -99,8 +99,10 @@
 #        cu_seq_k: torch.Tensor,
 #        max_q: int,
 #        max_k: int,
-#        is_causal: bool = False,
+#        *,
 #        return_aux: AuxRequest | None = None,
+#        scale: float | None = None,
+#        window_size: tuple[int, int] = (-1, -1),
 #    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
 #
 # ``query``, ``key``, and ``value`` correspond to the ``q``, ``k``, and
@@ -108,18 +110,22 @@
 # cumulative indices for query and key/value, respectively. These mark the
 # logical boundaries that separate the documents in our input. ``max_q``
 # and ``max_k`` are the maximum sequence lengths of query and key,
-# respectively. ``is_causal`` applies causal masking if set to True and
-# ``return_aux`` specifies which auxiliary outputs to return (ie ``lse``).
+# respectively. ``return_aux`` specifies which auxiliary outputs to return
+# (ie ``lse``). ``scale`` is an optional scaling factor applied to the
+# attention scores before softmax. ``window_size`` is a ``(left, right)``
+# tuple that controls sliding window attention: use ``(-1, -1)`` for full
+# attention (default), ``(-1, 0)`` for causal attention, or ``(W, 0)``
+# for causal attention with a sliding window of size ``W``.
 
 ######################################################################
 # **Note on causal masking**
-# When ``is_causal`` is set to True, causal masking is applied which means
-# that tokens can only attend to previous tokens. For bidirectional
-# attention, set this flag to False.
+# When ``window_size`` is set to ``(-1, 0)``, causal masking is applied
+# which means that tokens can only attend to previous tokens. For
+# bidirectional (full) attention, use the default ``(-1, -1)``.
 #
 # In torchtitan (PyTorch's pretraining framework), we set
-# ``is_causal = True`` uniformly to prevent the model from cheating and
-# artificially driving the loss down too quickly.
+# ``window_size = (-1, 0)`` uniformly to prevent the model from cheating
+# and artificially driving the loss down too quickly.
 
 
 ######################################################################
@@ -241,7 +247,7 @@ def forward(
             cu_seq_k=cu_seq,
             max_q=max_len,
             max_k=max_len,
-            is_causal=True,
+            window_size=(-1, 0),
         )
         attn_out = attn_out.view(-1, self.embed_dim)
         attn_out = self.out_proj(attn_out)