diff --git a/.lintrunner.toml b/.lintrunner.toml
index 0ed2cd453b6f..5a661d383542 100644
--- a/.lintrunner.toml
+++ b/.lintrunner.toml
@@ -38,7 +38,7 @@ init_command = [
     '--dry-run={{DRYRUN}}',
     'flake8==6.1.0',
     'flake8-bugbear==23.3.23',
-    'flake8-comprehensions==3.12.0',
+    'flake8-comprehensions==3.15.0',
     'flake8-executable==2.1.3',
     'flake8-logging-format==0.9.0',
     'flake8-pyi==23.3.1',
diff --git a/test/inductor/test_max_autotune.py b/test/inductor/test_max_autotune.py
index 7979cb552d63..2c4f635ddbc4 100644
--- a/test/inductor/test_max_autotune.py
+++ b/test/inductor/test_max_autotune.py
@@ -322,7 +322,7 @@ class TestMaxAutotune(TestCase):
                 return None
 
         fake_choices = [FakeChoiceCaller() for i in range(10)]
-        fake_lookup_result = {choice: 0.123 for choice in fake_choices}
+        fake_lookup_result = dict.fromkeys(fake_choices, 0.123)
 
         def no_lookup(
             choices: List[ChoiceCaller],
diff --git a/test/jit/test_list_dict.py b/test/jit/test_list_dict.py
index 637bd57853cb..47173d56653e 100644
--- a/test/jit/test_list_dict.py
+++ b/test/jit/test_list_dict.py
@@ -1645,9 +1645,9 @@ class TestDict(JitTestCase):
 
         def test_dictcomprehension_is_typed_from_annotation():
             metasyntactics = ["foo", "bar", "baz"]
-            x: Dict[str, Optional[int]] = {
+            x: Dict[str, Optional[int]] = {  # noqa: C420, RUF025
                 word: None for word in metasyntactics
-            }  # noqa: RUF025
+            }
             return x
 
         self.checkScript(test_dictcomprehension_is_typed_from_annotation, ())
diff --git a/torch/_dynamo/variables/nn_module.py b/torch/_dynamo/variables/nn_module.py
index 909bde78a5ea..f92458337672 100644
--- a/torch/_dynamo/variables/nn_module.py
+++ b/torch/_dynamo/variables/nn_module.py
@@ -921,7 +921,7 @@ class UnspecializedNNModuleVariable(UserDefinedObjectVariable):
             params_list = collect_parameters(self, recurse=recurse)
 
             # Account for duplicated params
-            deduplicated_params = list({param: None for param in params_list}.keys())
+            deduplicated_params = list(dict.fromkeys(params_list).keys())
 
             return variables.ListIteratorVariable(
                 deduplicated_params, mutable_local=MutableLocal()
diff --git a/torch/_inductor/dependencies.py b/torch/_inductor/dependencies.py
index 5d5f54a035a9..2bcda6dcaef9 100644
--- a/torch/_inductor/dependencies.py
+++ b/torch/_inductor/dependencies.py
@@ -69,7 +69,7 @@ class MemoryDep(Dep):
         """
         Return the offset by setting every variable to be 0.
         """
-        return sympy_subs(self.index, {v: 0 for v in self.var_names})
+        return sympy_subs(self.index, dict.fromkeys(self.var_names, 0))
 
     def normalize_with_stride_order(self, prefix="t"):
         r"""
diff --git a/torch/distributed/_tools/mem_tracker.py b/torch/distributed/_tools/mem_tracker.py
index e2439e22e115..c793f3e9b264 100644
--- a/torch/distributed/_tools/mem_tracker.py
+++ b/torch/distributed/_tools/mem_tracker.py
@@ -439,7 +439,7 @@ class MemTracker(TorchDispatchMode):
         maybe_zero = False
         # Ensure the device entry exists in the current memory snapshot, initializing if necessary.
         dev_snap = self._curr_mem_snap.setdefault(
-            winfo.device, {reftype: 0 for reftype in self._ref_class}
+            winfo.device, dict.fromkeys(self._ref_class, 0)
         )
         dev_snap.setdefault(_TOTAL_KEY, 0)
         # Handle different types of updates based on the update type (`u_type`).
diff --git a/torch/fx/passes/infra/partitioner.py b/torch/fx/passes/infra/partitioner.py
index e308ab81f0bd..271f90a7b75e 100644
--- a/torch/fx/passes/infra/partitioner.py
+++ b/torch/fx/passes/infra/partitioner.py
@@ -18,7 +18,7 @@ logger.setLevel(logging.WARNING)
 class Partition:
     def __init__(self, id: Optional[int] = None, nodes: Optional[Iterable[Node]] = None):
         self.id = id
-        self.nodes = {node: None for node in nodes} if nodes is not None else {}
+        self.nodes = dict.fromkeys(nodes) if nodes is not None else {}
 
     def __repr__(self) -> str:
         return str(self.nodes)