diff --git a/src/forge/actors/trainer.py b/src/forge/actors/trainer.py
index 4ffc63001..298c83489 100644
--- a/src/forge/actors/trainer.py
+++ b/src/forge/actors/trainer.py
@@ -47,6 +47,8 @@
 
 from forge.controller import ForgeActor
 from forge.data.utils import batch_to_device
+
+from forge.data_models.loss_metrics import LossMetrics
 from forge.observability.metrics import record_metric, Reduce
 from forge.observability.perf_tracker import Tracer
 
@@ -179,7 +181,7 @@ async def setup(self):
 
     def forward_backward(
         self, inputs: dict[str, Tensor], targets: dict[str, Tensor]
-    ) -> Tensor:
+    ) -> Tensor | LossMetrics:
         model_parts = self.engine.model_parts
         parallel_dims = self.engine.parallel_dims
 
@@ -236,12 +238,12 @@ def forward_backward(
                 assert len(model_parts) == 1
                 with self.engine.maybe_enable_amp:
                     logits = model_parts[0](**inputs)
-                    loss = self.loss(logits, **targets)
+                    loss, loss_metrics = self.loss(logits, **targets)
                 # need to free to before bwd to avoid peaking memory
                 del logits
                 loss.backward()
 
-        return loss
+        return loss, loss_metrics
 
     @endpoint
     async def train_step(
@@ -264,7 +266,7 @@ async def train_step(
         #     self.model,
         #     self.data_parallel_size,
         # ) as grad_acc:
-        loss = self.forward_backward(local_inputs, local_targets)
+        loss, loss_metrics = self.forward_backward(local_inputs, local_targets)
         torch.distributed.all_reduce(loss)
         t.step("forward_backward")
 
@@ -287,11 +289,24 @@ async def train_step(
         record_metric("rl_trainer/count_training_steps", 1, Reduce.SUM)
         record_metric("rl_trainer/avg_grpo_loss", loss, Reduce.MEAN)
 
-        # TODO: Extract actual KL divergence and policy entropy from the loss computation
-        # These are placeholder values until the loss function exposes these metrics
-        # record_metric("rl_trainer/step/avg_kl_divergence", 0.0, Reduce.MEAN)
-        # record_metric("rl_trainer/step/std_kl_divergence", 0.0, Reduce.STD)
-        # record_metric("rl_trainer/step/avg_policy_entropy", 0.0, Reduce.MEAN)
+        if loss_metrics.kl_divergence.sum().item() != 0:
+            record_metric(
+                "rl_trainer/step/avg_kl_divergence",
+                loss_metrics.kl_divergence,
+                Reduce.MEAN,
+            )
+            record_metric(
+                "rl_trainer/step/std_kl_divergence",
+                loss_metrics.kl_divergence,
+                Reduce.STD,
+            )
+
+        if loss_metrics.policy_entropy.sum().item() != 0:
+            record_metric(
+                "rl_trainer/step/avg_policy_entropy",
+                loss_metrics.policy_entropy,
+                Reduce.MEAN,
+            )
 
         self.step += 1
         self.engine.checkpointer.save(
diff --git a/src/forge/losses/grpo_loss.py b/src/forge/losses/grpo_loss.py
index 220367b47..8d4e85a03 100644
--- a/src/forge/losses/grpo_loss.py
+++ b/src/forge/losses/grpo_loss.py
@@ -7,6 +7,8 @@
 import torch
 from torch import nn
 
+from forge.data_models.loss_metrics import LossMetrics
+
 
 class SimpleGRPOLoss(nn.Module):
     """Simplified GRPO Loss for simplified single step updates
@@ -14,16 +16,27 @@ class SimpleGRPOLoss(nn.Module):
         https://github.com/huggingface/trl/blob/417915a3e4d3e3bc8d7b196594308b8eabf928be/trl/trainer/grpo_trainer.py#L1624.
     """
 
-    def __init__(self, beta: float = 0.1):
+    def __init__(self, beta: float = 0.1) -> tuple[torch.Tensor, LossMetrics]:
         super().__init__()
         self.beta = beta
 
-    def forward(self, logprobs, ref_logprobs, advantages, padding_mask):
+    def forward(
+        self, logprobs, ref_logprobs, advantages, padding_mask
+    ) -> tuple[torch.tensor, LossMetrics]:
         kl = torch.exp(ref_logprobs - logprobs) - (ref_logprobs - logprobs) - 1
+
         per_token_policy_loss = torch.exp(logprobs - logprobs.detach()) * advantages
         per_token_loss = -(per_token_policy_loss - self.beta * kl)
         loss = (
             ((per_token_loss * padding_mask).sum(dim=1))
             / (padding_mask.sum(dim=1).clamp(min=1.0))
         ).mean()
-        return loss
+
+        kl_padded = kl * padding_mask
+        # We want to save consistency with Reduce.MEAN in the trainer.
+        # Unfortunately, just kl * padding_mask is not working due to the different factor in the denominator
+        kl_padded = kl.sum(dim=1) / padding_mask.sum(dim=1).clamp(min=1.0)
+
+        return loss, LossMetrics(
+            kl_divergence=kl_padded, policy_entropy=torch.tensor(0)
+        )
diff --git a/src/forge/losses/reinforce_loss.py b/src/forge/losses/reinforce_loss.py
index c2dedd530..c79130a78 100644
--- a/src/forge/losses/reinforce_loss.py
+++ b/src/forge/losses/reinforce_loss.py
@@ -7,6 +7,8 @@
 import torch
 from torch import nn
 
+from forge.data_models.loss_metrics import LossMetrics
+
 from forge.util.ops import selective_log_softmax
 
 
@@ -28,7 +30,7 @@ def __init__(self):
 
     def forward(
         self, trainer_logits, target_ids, target_mask, target_weights, target_log_probs
-    ):
+    ) -> tuple[torch.Tensor, LossMetrics]:
         trainer_log_probs = selective_log_softmax(trainer_logits, target_ids)
         target_mask = target_mask.detach()
         target_weights = target_weights
@@ -47,4 +49,6 @@ def forward(
         numerator = (-trainer_log_probs * weighted_advantages * target_mask).sum()
 
         denominator = target_mask_sum
-        return numerator / denominator
+        return numerator / denominator, LossMetrics(
+            kl_divergence=torch.tensor(0), policy_entropy=torch.tensor(0)
+        )