diff --git a/nemo_rl/models/megatron/setup.py b/nemo_rl/models/megatron/setup.py
index e9fc2da9e1..b6ecd00483 100644
--- a/nemo_rl/models/megatron/setup.py
+++ b/nemo_rl/models/megatron/setup.py
@@ -588,7 +588,11 @@ def _create_megatron_config(
             global_batch_size=config["train_global_batch_size"],  # ignored
             train_iters=config["megatron_cfg"]["train_iters"],
         ),
-        optimizer=OptimizerConfig(**config["megatron_cfg"]["optimizer"]),
+        optimizer=OptimizerConfig(
+            fp8_recipe=config["megatron_cfg"]["fp8_cfg"]["fp8_recipe"],
+            overlap_param_gather=config["megatron_cfg"]["distributed_data_parallel_config"]["overlap_param_gather"],
+            **config["megatron_cfg"]["optimizer"]
+        ),
         ddp=DistributedDataParallelConfig(
             check_for_nan_in_grad=True,
             grad_reduce_in_fp32=config["megatron_cfg"][
@@ -609,6 +613,12 @@ def _create_megatron_config(
             data_parallel_sharding_strategy=config["megatron_cfg"][
                 "distributed_data_parallel_config"
             ]["data_parallel_sharding_strategy"],
+            fp8_param_gather=config["megatron_cfg"]["optimizer"].get(
+                "reuse_grad_buf_for_mxfp8_param_ag", False
+            ),
+            reuse_grad_buf_for_mxfp8_param_ag=config["megatron_cfg"]["optimizer"].get(
+                "reuse_grad_buf_for_mxfp8_param_ag", False
+            ),
         ),
         scheduler=SchedulerConfig(**config["megatron_cfg"]["scheduler"]),
         dataset=None,
diff --git a/nemo_rl/models/policy/workers/megatron_policy_worker.py b/nemo_rl/models/policy/workers/megatron_policy_worker.py
index d9a1c3d8a3..9cdbdcf8b6 100644
--- a/nemo_rl/models/policy/workers/megatron_policy_worker.py
+++ b/nemo_rl/models/policy/workers/megatron_policy_worker.py
@@ -326,6 +326,20 @@ def train(
                     self.model.zero_grad_buffer()
                     self.optimizer.zero_grad()
 
+                    from megatron.bridge.training.train import (
+                        _handle_mxfp8_param_buffer_copy,
+                    )
+
+                    _handle_mxfp8_param_buffer_copy(
+                        optimizer=self.optimizer,
+                        reuse_grad_buf_for_mxfp8_param_ag=self.cfg["megatron_cfg"][
+                            "optimizer"
+                        ]["reuse_grad_buf_for_mxfp8_param_ag"],
+                        overlap_param_gather=self.cfg["megatron_cfg"][
+                            "distributed_data_parallel_config"
+                        ]["overlap_param_gather"],
+                    )
+
                     # Forward pass.
                     losses_reduced = megatron_forward_backward(
                         model=self.model,
@@ -463,6 +477,22 @@ def get_logprobs(
           We use the convention that the logprob of the first token is 0 so that the sequence length is maintained.
           The logprob of input token i is specified at position i in the output logprobs tensor.
         """
+        self.model.zero_grad_buffer()
+
+        from megatron.bridge.training.train import (
+            _handle_mxfp8_param_buffer_copy,
+        )
+
+        _handle_mxfp8_param_buffer_copy(
+            optimizer=self.optimizer,
+            reuse_grad_buf_for_mxfp8_param_ag=self.cfg["megatron_cfg"][
+                "optimizer"
+            ]["reuse_grad_buf_for_mxfp8_param_ag"],
+            overlap_param_gather=self.cfg["megatron_cfg"][
+                "distributed_data_parallel_config"
+            ]["overlap_param_gather"],
+        )
+
         no_grad = torch.no_grad()
         no_grad.__enter__()
         logprob_batch_size = (
@@ -1057,13 +1087,13 @@ def broadcast_weights_for_collective(
         )
 
     def prepare_for_lp_inference(self):
-        self.model = self.move_model(self.model, "cuda", move_grads=False)
+        self.model = self.move_model(self.model, "cuda", move_grads=True)
         self.model.eval()
 
-        # offload grads to cpu
-        self.model = self.move_model(
-            self.model, "cpu", move_params=False, move_grads=True
-        )  # get rid of grad buffers
+        # # offload grads to cpu
+        # self.model = self.move_model(
+        #     self.model, "cpu", move_params=False, move_grads=True
+        # )  # get rid of grad buffers
 
         # offload optimizer to cpu
         torch.randn(1).cuda()  # wake up torch allocator