diff --git a/fast_llm/functional/entropy_loss.py b/fast_llm/functional/entropy_loss.py
index 757832a7..f1212f4b 100644
--- a/fast_llm/functional/entropy_loss.py
+++ b/fast_llm/functional/entropy_loss.py
@@ -121,7 +121,7 @@ def _fused_reverse_kl_base(
     # Compute loss terms: student_probs * log_ratio, then sum over vocab
     # This is equivalent to kl_div(..., log_target=True) but more memory efficient
     log_ratio = predicted_log_probability - target_log_probability
-    per_sample_loss = (predicted_probability * log_ratio).sum(dim=-1)
+    per_sample_loss = (predicted_probability * log_ratio).sum(dim=-1, keepdim=True)
     if group is not None:
         all_reduce(per_sample_loss, op=ReduceOp.SUM, group=group)
 
@@ -130,7 +130,7 @@ def _fused_reverse_kl_base(
     else:
         # Gradient: d/d(logits) KL(q||p) = q * (log(q/p) - E_q[log(q/p)])
         # where E_q[log(q/p)] is the expected log ratio under the student distribution
-        grad = (log_ratio - per_sample_loss.unsqueeze(-1)) * predicted_probability * grad_output
+        grad = (log_ratio - per_sample_loss) * predicted_probability * grad_output
 
     return per_sample_loss, grad
 
diff --git a/tests/functional/test_entropy_loss.py b/tests/functional/test_entropy_loss.py
index 9c06c191..35d1ef64 100644
--- a/tests/functional/test_entropy_loss.py
+++ b/tests/functional/test_entropy_loss.py
@@ -82,14 +82,13 @@ def test_entropy_loss(num_columns, grad_output, logits_scale_factor, loss_maskin
     out_torch, grad_torch = entropy_loss_forward_backward(**kwargs, implementation=EntropyLossImplementation.torch)
     out_fused, grad_fused = entropy_loss_forward_backward(**kwargs, implementation=EntropyLossImplementation.fused)
 
-    # TODO: Why is the error so high with loss masking for reverse KL?
     _compare_entropy_loss_outputs(
         out_fused,
         out_torch,
         grad_output is not None,
         grad_fused,
         grad_torch,
-        loss_min_threshold=2e-4 if entropy_loss_type == EntropyLossType.reverse_kl and loss_masking else 5e-6,
+        loss_min_threshold=5e-6,
     )
 
     if entropy_loss_type != EntropyLossType.cross_entropy or not torch.cuda.is_available():