diff --git a/apps/grpo/main.py b/apps/grpo/main.py
index 7f31c26c9..ae31af5e1 100644
--- a/apps/grpo/main.py
+++ b/apps/grpo/main.py
@@ -249,7 +249,7 @@ async def main(cfg: DictConfig):
     )
 
     # ---- Setup services ---- #
-    await ts.initialize()
+    await ts.initialize(strategy=ts.ControllerStorageVolumes())
     (
         dataloader,
         policy,
diff --git a/src/forge/actors/policy.py b/src/forge/actors/policy.py
index 070c00798..28febad25 100644
--- a/src/forge/actors/policy.py
+++ b/src/forge/actors/policy.py
@@ -15,6 +15,7 @@
 from dataclasses import asdict, dataclass, field, fields
 
 import torch
+import torch.distributed.checkpoint as dcp
 import torchstore as ts
 from monarch.actor import current_rank, endpoint, ProcMesh
 from torchstore.state_dict_utils import DELIM
@@ -399,6 +400,7 @@ async def stop(self):
 class PolicyWorker(ForgeActor):
     vllm_config: VllmConfig
     state_dict_key: str = "model_state_dict"
+    use_dcp: bool = True
 
     @endpoint
     async def setup(self):
@@ -420,14 +422,26 @@ async def _load_tensor_parallel_state_dict(
             self.vllm_config.parallel_config.tensor_parallel_size, self.rank
         )
 
+        checkpoint_id = f"{self.state_dict_key}{DELIM}{version}"
+        dcp_metadata = None
+        if self.use_dcp:
+            dcp_metadata = await ts.get(checkpoint_id)
+
         for param_name in current_state_dict.keys():
             current_tensor = current_state_dict[param_name]
 
             # Load the full tensor from torchstore
             # TODO: only get the part of the tensor that is needed
-            stored_tensor = await ts.get(
-                f"{self.state_dict_key}{DELIM}{version}{DELIM}{param_name}"
-            )
+            if self.use_dcp:
+                tensor_meta = dcp_metadata.state_dict_metadata[param_name]
+                stored_tensor = torch.empty(
+                    size=tensor_meta.size, dtype=tensor_meta.properties.dtype
+                )
+                dcp.load(
+                    checkpoint_id=checkpoint_id, state_dict={param_name: stored_tensor}
+                )
+            else:
+                stored_tensor = await ts.get(f"{checkpoint_id}{DELIM}{param_name}")
             sharding.load_from_source_to_target(
                 param_name,
                 stored_tensor,
diff --git a/src/forge/actors/trainer.py b/src/forge/actors/trainer.py
index 0368fac4d..5dffbac1e 100644
--- a/src/forge/actors/trainer.py
+++ b/src/forge/actors/trainer.py
@@ -12,6 +12,7 @@
 from typing import Callable
 
 import torch
+import torch.distributed.checkpoint as dcp
 import torchstore as ts
 
 from monarch.actor import current_rank, current_size, endpoint
@@ -53,6 +54,7 @@ class RLTrainer(ForgeActor):
     comm: Comm = field(default_factory=Comm)
     loss: Callable = lambda logits, **targets: logits
     state_dict_key: str = "model_state_dict"
+    use_dcp: bool = True
 
     def __post_init__(self):
         """Initializes config types and env variables.
@@ -95,7 +97,7 @@ def __post_init__(self):
     async def setup(self):
         # TODO: update ForgeEngine to not use ForgeJobConfig
         engine_config = {f.name: getattr(self, f.name) for f in fields(self)}
-        for key in {"loss", "state_dict_key"}:
+        for key in {"loss", "state_dict_key", "use_dcp"}:
             engine_config.pop(key)  # Not part of job config
         self.engine = ForgeEngine(ForgeJobConfig(**engine_config))
         self.engine.checkpointer.load(step=self.current_step)
@@ -207,6 +209,7 @@ async def push_weights(self, policy_version: int) -> None:
         # 2. Unify CheckpointManager and TorchStore weights save control path.
         if "model" not in self.engine.checkpointer.states:
             raise RuntimeError("Model state not found in checkpointer state")
+
         sd = self.engine.checkpointer.states["model"].state_dict()
         flattened_state_dict, _ = flatten_state_dict(sd)
         if self.engine.checkpointer.sd_adapter is None:
@@ -216,10 +219,16 @@ async def push_weights(self, policy_version: int) -> None:
         hf_state_dict = self.engine.checkpointer.sd_adapter.to_hf(flattened_state_dict)
         # TODO: Figure out how to gracefully handle which model to-vLLM conversion is needed
         vllm_ready_hf_sd = _qwen3_hf_to_vllm(sd=hf_state_dict, num_layers=28)
+
         key = f"{self.state_dict_key}{DELIM}{policy_version}"
         start_time = time.time()
-        await ts.put_state_dict(state_dict=vllm_ready_hf_sd, key=key)
+        if self.use_dcp:
+            metadata = dcp.save(checkpoint_id=key, state_dict=vllm_ready_hf_sd)
+            await ts.put(key, metadata)
+        else:
+            await ts.put_state_dict(vllm_ready_hf_sd, key)
         end_time = time.time()
+
         self.logger.debug(
             f"Pushed weights to {key} in {end_time - start_time:.2f} seconds"
         )