diff --git a/tests/test_sft_trainer.py b/tests/test_sft_trainer.py
index 9703fad09a..cde6ee76a5 100644
--- a/tests/test_sft_trainer.py
+++ b/tests/test_sft_trainer.py
@@ -1342,8 +1342,14 @@ def test_train_vlm_multi_image(self):
             new_param = trainer.model.get_parameter(n)
             assert not torch.allclose(param, new_param, rtol=1e-12, atol=1e-12), f"Param {n} is not updated"
 
+    @parameterized.expand(
+        [
+            ("trl-internal-testing/tiny-Gemma3ForConditionalGeneration",),
+            ("trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",),
+        ]
+    )
     @require_vision
-    def test_train_vlm_prompt_completion(self):
+    def test_train_vlm_prompt_completion(self, model_id):
         # Get the dataset
         dataset = load_dataset("trl-internal-testing/zen-image", "conversational_prompt_completion", split="train")
 
@@ -1354,7 +1360,7 @@ def test_train_vlm_prompt_completion(self):
             report_to="none",
         )
         trainer = SFTTrainer(
-            model="trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration",
+            model=model_id,
             args=training_args,
             train_dataset=dataset,
         )
diff --git a/trl/trainer/sft_trainer.py b/trl/trainer/sft_trainer.py
index 3f0509a9ae..032d12fb66 100644
--- a/trl/trainer/sft_trainer.py
+++ b/trl/trainer/sft_trainer.py
@@ -418,34 +418,51 @@ def _collate_prompt_completion(self, examples: list[dict[str, Any]]) -> dict[str
             add_special_tokens=False,  # to avoid adding the BOS, twice see https://huggingface.co/blog/qgallouedec/gotchas-in-tokenizer-behavior#7-chat-template-and-tokenization-dont-compose-due-to-special-tokens
         )
 
+        to_concat_keys = {"input_ids", "attention_mask"}
+        for candidate_key in (processed_prompts.keys() & processed_completions.keys()) - to_concat_keys:
+            if (
+                processed_prompts[candidate_key].shape == processed_prompts["attention_mask"].shape
+                and processed_completions[candidate_key].shape == processed_completions["attention_mask"].shape
+            ):
+                to_concat_keys.add(candidate_key)
+
         # Concatenate prompts and completions
-        prompt_ids, completion_ids = processed_prompts["input_ids"], processed_completions["input_ids"]
-        prompt_mask, completion_mask = processed_prompts["attention_mask"], processed_completions["attention_mask"]
-        input_ids = torch.cat((prompt_ids, completion_ids), dim=1)
-        attention_mask = torch.cat((prompt_mask, completion_mask), dim=1)
-        completion_mask = torch.cat((torch.zeros_like(prompt_mask), completion_mask), dim=1)
+        prompt_completion_concats = {
+            key: torch.cat((processed_prompts[key], processed_completions[key]), dim=1) for key in to_concat_keys
+        }
+        prompt_completion_concats["completion_mask"] = torch.cat(
+            (torch.zeros_like(processed_prompts["attention_mask"]), processed_completions["attention_mask"]), dim=1
+        )
 
         # Flush left to reduce padding
-        attention_mask, input_ids, completion_mask = flush_left(attention_mask, input_ids, completion_mask)
+        non_attention_mask_keys = prompt_completion_concats.keys() - {"attention_mask"}
+        prompt_completion_concats = dict(
+            zip(
+                ["attention_mask", *non_attention_mask_keys],
+                flush_left(
+                    prompt_completion_concats["attention_mask"],
+                    *(prompt_completion_concats[key] for key in non_attention_mask_keys),
+                ),
+            )
+        )
 
         # Truncate if necessary
         if self.max_length is not None:
-            input_ids = input_ids[:, : self.max_length]
-            attention_mask = attention_mask[:, : self.max_length]
-            completion_mask = completion_mask[:, : self.max_length]
+            prompt_completion_concats = {
+                key: val[:, : self.max_length] for key, val in prompt_completion_concats.items()
+            }
 
         # Create labels and mask padding tokens
-        labels = input_ids.clone()
-        labels[attention_mask == 0] = -100
+        labels = prompt_completion_concats["input_ids"].clone()
+        labels[prompt_completion_concats["attention_mask"] == 0] = -100
         if self.completion_only_loss:
-            labels[completion_mask == 0] = -100
+            labels[prompt_completion_concats["completion_mask"] == 0] = -100
+        prompt_completion_concats["labels"] = labels
 
         # Build the output dictionary
-        output = processed_prompts  # we take processed_prompts because it contains the images
-        output["input_ids"] = input_ids
-        output["attention_mask"] = attention_mask
-        output["labels"] = labels
-        return output
+        return (
+            processed_prompts | prompt_completion_concats
+        )  # we take processed_prompts because it contains the images
 
 
 def dft_loss(outputs, labels, num_items_in_batch=None):