diff --git a/areal/api/cli_args.py b/areal/api/cli_args.py
index ea55f557a8..34bae678ee 100644
--- a/areal/api/cli_args.py
+++ b/areal/api/cli_args.py
@@ -874,6 +874,17 @@ class MegatronEngineConfig:
     # Checkpointing Configuration
     async_save: bool = False
     use_checkpoint_opt_param_scheduler: bool = True
+    distrib_optim_sharding_type: str = field(
+        default="dp_reshardable",
+        metadata={
+            "help": (
+                "Sharding type for distributed optimizer checkpoint. "
+                "'dp_reshardable' works with megatron-core >=0.11; set to "
+                "'fully_sharded_model_space' to load legacy checkpoints."
+            ),
+            "choices": ["dp_reshardable", "fully_sharded_model_space"],
+        },
+    )
 
     # Deterministic Option
     # NOTE: This option forces torch to use deterministic algorithms,
diff --git a/areal/engine/megatron_engine.py b/areal/engine/megatron_engine.py
index a512469bc0..57176265be 100644
--- a/areal/engine/megatron_engine.py
+++ b/areal/engine/megatron_engine.py
@@ -1358,6 +1358,7 @@ def _create_optimizer(self, ft_spec: FinetuneSpec) -> None:
                 use_distributed_optimizer=use_distributed_optimizer,
                 use_checkpoint_opt_param_scheduler=self.mcore_config.use_checkpoint_opt_param_scheduler,
                 async_save=self.mcore_config.async_save,
+                distrib_optim_sharding_type=self.mcore_config.distrib_optim_sharding_type,
             )
 
     def _check_rollout_engine_connected(self) -> None:
diff --git a/areal/engine/megatron_utils/checkpointer.py b/areal/engine/megatron_utils/checkpointer.py
index 78a32867f5..dc1e382ae8 100644
--- a/areal/engine/megatron_utils/checkpointer.py
+++ b/areal/engine/megatron_utils/checkpointer.py
@@ -147,6 +147,7 @@ def __init__(
         use_checkpoint_opt_param_scheduler: bool = False,
         use_dist_checkpointing: bool = True,
         async_save: bool = False,
+        distrib_optim_sharding_type: str = "dp_reshardable",
     ):
         self.model = model
         self.optimizer = optimizer
@@ -160,6 +161,7 @@ def __init__(
         self.rank = torch.distributed.get_rank()
         self.use_dist_checkpointing = use_dist_checkpointing
         self.async_save = async_save
+        self.distrib_optim_sharding_type = distrib_optim_sharding_type
         if async_save:
             raise NotImplementedError("Async save not implenmented yet!")
 
@@ -280,7 +282,17 @@ def generate_state_dict(
         # Optimizer State Dict
         if with_optimizer:
             torch.distributed.barrier()
-            optimizer_sharded_states = self.optimizer.sharded_state_dict(state_dict)
+            # megatron-core >=0.11 removed flattened_range support in
+            # ShardedTensor.validate_metadata_integrity(), but the default
+            # sharding type (fully_sharded_model_space) still sets
+            # flattened_range, causing save/load to fail. Use
+            # dp_reshardable which does not rely on flattened_range.
+            optimizer_sharded_states = self.optimizer.sharded_state_dict(
+                state_dict,
+                metadata={
+                    "distrib_optim_sharding_type": self.distrib_optim_sharding_type
+                },
+            )
             state_dict["optimizer"] = optimizer_sharded_states
 
             if self.lr_scheduler is not None: