diff --git a/tests/test_grpo_trainer.py b/tests/test_grpo_trainer.py
index 3002ce2b7fa..7e41b48a813 100644
--- a/tests/test_grpo_trainer.py
+++ b/tests/test_grpo_trainer.py
@@ -703,7 +703,12 @@ def test_training_beta_non_zero(self):
             new_param = trainer.model.get_parameter(n)
             assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
-    def test_training_with_cast_lm_head_to_fp32(self):
+    @pytest.mark.parametrize(
+        "model_name",
+        ["trl-internal-testing/tiny-Qwen3ForCausalLM", "trl-internal-testing/tiny-Gemma2ForCausalLM"],
+        # Gemma2 has the input word embeddings and lm_head tied, Qwen3 does not
+    )
+    def test_training_with_cast_lm_head_to_fp32(self, model_name):
         dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
         training_args = GRPOConfig(
             output_dir=self.tmp_dir,
@@ -715,7 +720,7 @@ def test_training_with_cast_lm_head_to_fp32(self):
             cast_lm_head_to_fp32=True,
         )
         trainer = GRPOTrainer(
-            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            model=model_name,
             reward_funcs="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
             args=training_args,
             train_dataset=dataset,
diff --git a/trl/trainer/grpo_trainer.py b/trl/trainer/grpo_trainer.py
index 8d9492eed8a..b6b89e6d427 100644
--- a/trl/trainer/grpo_trainer.py
+++ b/trl/trainer/grpo_trainer.py
@@ -479,21 +479,31 @@ def __init__(
 
         # Cast LM Head To FP32
         if args.cast_lm_head_to_fp32:
-            if not model.config.tie_word_embeddings:
 
-                def cast_inputs_to_fp32(module, input):
-                    return (input[0].float(),)
+            def _cast_lm_head_to_fp32(target_model: PreTrainedModel):
+                """Cast lm_head to fp32 while preserving embedding output dtype if tied."""
 
-                model.lm_head = model.lm_head.float()
-                model.lm_head.register_forward_pre_hook(cast_inputs_to_fp32)
-                if self.ref_model is not None:
-                    self.ref_model.lm_head = self.ref_model.lm_head.float()
-                    self.ref_model.lm_head.register_forward_pre_hook(cast_inputs_to_fp32)
-            else:
-                raise NotImplementedError(
-                    "`cast_lm_head_to_fp32=True` is only supported when the model has untied word embedding and language modeling head layers"
-                    "i.e. `tie_word_embeddings` in the model config is False."
-                )
+                def cast_inputs_to_fp32(module, inputs):
+                    # Preserve other positional args and kwargs untouched
+                    if not inputs:
+                        return inputs
+                    return (inputs[0].to(torch.float32),) + inputs[1:]
+
+                original_dtype_local = target_model.lm_head.weight.dtype
+                target_model.lm_head = target_model.lm_head.float()
+                target_model.lm_head.register_forward_pre_hook(cast_inputs_to_fp32)
+
+                if target_model.config.tie_word_embeddings:
+
+                    def cast_outputs_to_original_dtype(module, args, output):
+                        return output.to(original_dtype_local)
+
+                    # Only cast activations; weights are now fp32 (intentional for numerical stability of logits)
+                    target_model.model.embed_tokens.register_forward_hook(cast_outputs_to_original_dtype)
+
+            _cast_lm_head_to_fp32(model)
+            if self.ref_model is not None:
+                _cast_lm_head_to_fp32(self.ref_model)
 
         # Liger loss
         if self.use_liger_kernel: