diff --git a/thunder/constants.py b/thunder/constants.py
index 5a49f674a0..29d6e8c292 100644
--- a/thunder/constants.py
+++ b/thunder/constants.py
@@ -5,3 +5,4 @@
 # PyTorch version constants
 # `use_base_version` so that dev builds like "2.13.0a0+gitabc123" compare as "2.13.0"
 _TORCH_GREATER_EQUAL_2_13 = compare_version("torch", operator.ge, "2.13.0", use_base_version=True)
+_TORCH_GREATER_EQUAL_2_14 = compare_version("torch", operator.ge, "2.14.0", use_base_version=True)
diff --git a/thunder/core/jit_ext.py b/thunder/core/jit_ext.py
index 95b38e3d94..18fb6792dc 100644
--- a/thunder/core/jit_ext.py
+++ b/thunder/core/jit_ext.py
@@ -1009,7 +1009,11 @@ def _generate_random_str_id() -> str:
             continue
         trace_of_forward.bound_symbols.append(bsym.from_bsym())
     with tracectx(trace_of_forward):
-        prims.python_return(*(sequencify(output)))
+        # Keep the fwd graph's output structure. Since torch 2.12 it returns a tuple, which the caller indexes
+        if isinstance(output, (tuple, list)):
+            prims.python_return(tuple(output))
+        else:
+            prims.python_return(output)
 
     # See NOTE: `autograd_function_apply` and `no_grad` interaction for details about
     # `thunder.torch.call_higher_order_function_and_consider_outer_autograd_setting`
diff --git a/thunder/tests/framework.py b/thunder/tests/framework.py
index 7fa3b1485b..5265ddfe1a 100644
--- a/thunder/tests/framework.py
+++ b/thunder/tests/framework.py
@@ -58,6 +58,12 @@ class NOTHING:
 )
 IS_WINDOWS = platform.system() == "Windows"
 
+# NOTE Running these models in bfloat16 on CPU intermittently kills the process on Windows with an
+#   illegal instruction, inside eager torch rather than anything of ours. The fp32 variants of the
+#   same models still run there, and these still run on Linux and macOS.
+# TODO: Remove this skip once the crash is fixed upstream, or pinned to a oneDNN/MKL code path we can avoid.
+WINDOWS_GPT_CRASH_REASON = "bfloat16 CPU model intermittently crashes the process on Windows (0xc000001d)"
+
 
 def _bitsandbytes_available():
     if not package_available("bitsandbytes"):
diff --git a/thunder/tests/opinfos.py b/thunder/tests/opinfos.py
index f6997926de..a39cc5fa2c 100644
--- a/thunder/tests/opinfos.py
+++ b/thunder/tests/opinfos.py
@@ -23,6 +23,7 @@
 import thunder.core.prims as prims
 from thunder.core.pytree import tree_map
 from thunder.core.symbol import Symbol
+from thunder.constants import _TORCH_GREATER_EQUAL_2_14
 import thunder.executors as executors
 from thunder.tests.framework import _all_devicetypes, custom_comparator, IS_WINDOWS
 from thunder.tests.make_tensor import make_tensor, make_tensor_like
@@ -8574,6 +8575,10 @@ def group_norm_sample_generator(op, device, dtype, requires_grad, **kwargs):
             if torch.device(device).type == "cuda" and ndim >= 3 and num_channels == 0:
                 continue
 
+            # torch 2.14 rejects empty channel or spatial dims (an empty batch is still fine)
+            if _TORCH_GREATER_EQUAL_2_14 and (num_channels == 0 or math.prod(inner_dims) == 0):
+                continue
+
             a = make(shape)
 
             for weight, bias in itertools.product((False, True), repeat=2):
diff --git a/thunder/tests/test_interpreter.py b/thunder/tests/test_interpreter.py
index c545954069..fe36f26e64 100644
--- a/thunder/tests/test_interpreter.py
+++ b/thunder/tests/test_interpreter.py
@@ -11,7 +11,7 @@
 import pytest
 import torch
 from torch.testing import assert_close
-from thunder.tests.framework import IS_WINDOWS
+from thunder.tests.framework import IS_WINDOWS, WINDOWS_GPT_CRASH_REASON
 
 import thunder
 from thunder.core.interpreter import (
@@ -3413,6 +3413,7 @@ def test_nanogpt_block(jit):
     assert_close(result, fn(*args, **kwargs))
 
 
+@pytest.mark.skipif(IS_WINDOWS, reason=WINDOWS_GPT_CRASH_REASON)
 def test_nanogpt(jit):
     from thunder.benchmarks import NanoGPTBenchmark, NanoGPTConfig, _nanogpt_configs
 
@@ -3428,6 +3429,7 @@ def test_nanogpt(jit):
     assert_close(result, fn(*args, **kwargs))
 
 
+@pytest.mark.skipif(IS_WINDOWS, reason=WINDOWS_GPT_CRASH_REASON)
 def test_litgpt(jit):
     from thunder.benchmarks import LitGPTBenchmark
     from thunder.tests.litgpt_model import Config
diff --git a/thunder/tests/test_jit_general.py b/thunder/tests/test_jit_general.py
index 15a62703ee..22b42e33d4 100644
--- a/thunder/tests/test_jit_general.py
+++ b/thunder/tests/test_jit_general.py
@@ -14,7 +14,7 @@
 
 import thunder
 
-from thunder.tests.framework import requiresCUDA, IS_WINDOWS
+from thunder.tests.framework import requiresCUDA, IS_WINDOWS, WINDOWS_GPT_CRASH_REASON
 from thunder.core.options import CACHE_OPTIONS
 import thunder.core.prims as prims
 from thunder import pytorch_executor, nvfuser_executor
@@ -47,6 +47,9 @@ def _autograd_function_apply_kwargs(args_tensor_mask, non_differentiable_idx=Non
     kwargs = {}
     if _HAS_ARGS_TENSOR_MASK:
         kwargs["args_tensor_mask"] = args_tensor_mask
+    else:
+        # required since torch 2.11; the tests here save only x
+        kwargs["saved_for_backward_idx"] = [0]
     if non_differentiable_idx is not None:
         kwargs["non_differentiable_idx"] = non_differentiable_idx
     return kwargs
@@ -581,6 +584,7 @@ def h(d, c):
     assert args_names == ("a", "b", "c", "d")
 
 
+@pytest.mark.skipif(IS_WINDOWS, reason=WINDOWS_GPT_CRASH_REASON)
 def test_litgpt():
     from thunder.benchmarks import LitGPTBenchmark
     from thunder.tests.litgpt_model import Config
@@ -661,6 +665,7 @@ def test_nanogpt_mlp():
     assert_close(result, module(*args, **kwargs))
 
 
+@pytest.mark.skipif(IS_WINDOWS, reason=WINDOWS_GPT_CRASH_REASON)
 def test_nanogpt():
     from thunder.benchmarks import NanoGPTBenchmark, NanoGPTConfig, _nanogpt_configs
 
diff --git a/thunder/torch/__init__.py b/thunder/torch/__init__.py
index 74ae2ba269..417043b2b0 100644
--- a/thunder/torch/__init__.py
+++ b/thunder/torch/__init__.py
@@ -6715,6 +6715,8 @@ def autograd_function_apply(
     *args: Any,
     args_tensor_mask: Sequence[bool] | None = None,
     non_differentiable_idx: Sequence[int] | None = None,
+    saved_for_backward_idx: Sequence[int] | None = None,  # unused, passed by torch >= 2.11
+    dirty_idx: Sequence[int] | None = None,  # unused, passed by torch >= 2.13
 ) -> TensorProxy | tuple[TensorProxy, ...]:
     # TODO: Remove this once this autograd API becomes stable.
     # On stable PyTorch, fwd expects ctx as first argument
@@ -6733,6 +6735,8 @@ def augmented_forward_autograd_function_apply(
     *args: Any,
     args_tensor_mask: Sequence[bool] | None = None,
     non_differentiable_idx: Sequence[int] | None = None,
+    saved_for_backward_idx: Sequence[int] | None = None,  # unused, passed by torch >= 2.11
+    dirty_idx: Sequence[int] | None = None,  # unused, passed by torch >= 2.13
 ) -> tuple[TensorProxy | tuple[TensorProxy, ...], tuple[Any, ...]]:
     # TODO: Remove this once this autograd API becomes stable.
     # On stable PyTorch, fwd expects ctx as first argument