diff --git a/.github/workflows/integration_test_8gpu.yaml b/.github/workflows/integration_test_8gpu.yaml
index bf534746f..d5db8b0eb 100644
--- a/.github/workflows/integration_test_8gpu.yaml
+++ b/.github/workflows/integration_test_8gpu.yaml
@@ -36,6 +36,8 @@ jobs:
 
         pip config --user set global.progress_bar off
 
+        git clone https://github.com/soulitzer/ac-experimental.git && cd ac-experimental && pip install -e . && cd ..
+
         python -m pip install --force-reinstall --pre torch --index-url https://download.pytorch.org/whl/nightly/cu126
 
         USE_CPP=0 python -m pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
diff --git a/torchtitan/models/llama3/parallelize_llama.py b/torchtitan/models/llama3/parallelize_llama.py
index 64dfcad23..28bb6d0ba 100644
--- a/torchtitan/models/llama3/parallelize_llama.py
+++ b/torchtitan/models/llama3/parallelize_llama.py
@@ -12,10 +12,36 @@
 import torch
 import torch.nn as nn
 from torch.distributed._composable.replicate import replicate
+
 from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-    checkpoint_wrapper as ptd_checkpoint_wrapper,
+    ActivationWrapper,
 )
 
+
+class CheckpointWrapper(ActivationWrapper):
+    def __init__(self, mod: torch.nn.Module, **kwargs):
+        super().__init__(mod)
+        self._checkpoint_wrapped_module = mod
+        self._make_policy_fn = kwargs.get("make_policy_fn", None)
+
+    def forward(self, *args, **kwargs):
+        from ac_experimental import apply_ac_policy_fn
+
+        if self._make_policy_fn is None:
+            return apply_ac_policy_fn(
+                self._checkpoint_wrapped_module, *args, **kwargs, policy_fn="recompute_all"
+            )
+        else:
+            # Pass is_factory=True so that a new instance of policy_fn is created per AC invocation
+            return apply_ac_policy_fn(
+                self._checkpoint_wrapped_module, *args, **kwargs, policy_fn=self._make_policy_fn, is_factory=True
+            )
+
+
+def ptd_checkpoint_wrapper(mod, **kwargs):
+    return CheckpointWrapper(mod, **kwargs)
+
+
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.fsdp import CPUOffloadPolicy, fully_shard, MixedPrecisionPolicy
 from torch.distributed.tensor import Replicate, Shard
@@ -226,6 +252,29 @@ def apply_tp(
     torch.ops.aten.max.default,
 }
 
+from torch.utils.checkpoint import CheckpointPolicy
+
+# If you want your policy to have state, pass a class. Make sure to
+# create it in global scope to avoid new instances triggering recompiles.
+class CustomPolicy:
+    def __init__(self):
+        super().__init__()
+        self.meta = dict()
+
+    def __call__(self, ctx, out, func, *args, **kwargs):
+        mm_count_key = f"mm_count"
+        if func == torch.ops.aten.mm.default:
+            self.meta[mm_count_key] = self.meta.get(mm_count_key, 0) + 1
+
+        # Saves output of all compute ops, except every second mm
+        to_save = func in _save_list and not (
+            func == torch.ops.aten.mm.default and self.meta[mm_count_key] % 2 == 0
+        )
+        return (
+            CheckpointPolicy.MUST_SAVE
+            if to_save
+            else CheckpointPolicy.PREFER_RECOMPUTE
+        )
 
 def _apply_ac_to_transformer_block(module: nn.Module, ac_config):
     valid_ac_modes = ("full", "selective")
@@ -246,38 +295,11 @@ def _apply_ac_to_transformer_block(module: nn.Module, ac_config):
             f"Valid options: 'op' or a positive int representing layer frequency"
         )
     if use_op_sac:
-        from torch.utils.checkpoint import (
-            CheckpointPolicy,
-            create_selective_checkpoint_contexts,
-        )
-
-        def _get_custom_policy(meta):
-            def _custom_policy(ctx, func, *args, **kwargs):
-                mode = "recompute" if ctx.is_recompute else "forward"
-                mm_count_key = f"{mode}_mm_count"
-                if func == torch.ops.aten.mm.default:
-                    meta[mm_count_key] += 1
-                # Saves output of all compute ops, except every second mm
-                to_save = func in _save_list and not (
-                    func == torch.ops.aten.mm.default and meta[mm_count_key] % 2 == 0
-                )
-                return (
-                    CheckpointPolicy.MUST_SAVE
-                    if to_save
-                    else CheckpointPolicy.PREFER_RECOMPUTE
-                )
-
-            return _custom_policy
-
-        def selective_checkpointing_context_fn():
-            meta = defaultdict(int)
-            return create_selective_checkpoint_contexts(_get_custom_policy(meta))
-
         return ptd_checkpoint_wrapper(
             module,
-            context_fn=selective_checkpointing_context_fn,
-            preserve_rng_state=False,
+            make_policy_fn=CustomPolicy,
         )
+
     elif use_layer_sac:
         # Checkpoint every `ac_freq` of the modules passed to this function
         ac_freq = int(ac_config.selective_ac_option)