Simplify _get_tool_suffix_ids

tests/test_data_utils.py

@@ -215,7 +215,7 @@ def test_message_with_tool_calling_turns(self):
                     }
                 ],
             },
-            {"role": "tool", "name": "get_current_weather", "content": "22.0"},
+            {"role": "tool", "name": "get_current_weather", "content": [{"type": "text", "text": "22.0"}]},
             {"role": "assistant", "content": "The current weather in New York is 22.0 degrees Celsius."},
         ]
 

trl/data_utils.py

@@ -91,9 +91,8 @@ def prepare_multimodal_messages(messages: list[dict[str, Any]], images: list | N
             if message.get("content") and isinstance(message["content"], str):
                 message["content"] = [{"type": "text", "text": message["content"]}]
         elif message["role"] == "tool":
-            # NOTE: `tool` contains `name` (name of the tool used) and `content` (output of the tool call as a string)
-            # but there's no need to prepare it for multimodal specifically but rather leave it as-is
-            continue
+            if message.get("content") and isinstance(message["content"], str):
+                message["content"] = [{"type": "text", "text": message["content"]}]
         else:
             raise ValueError(
                 f"Invalid role in message: {message['role']}. Expected 'system', 'user', 'assistant', or 'tool'."

trl/trainer/grpo_trainer.py

@@ -1417,6 +1417,8 @@ def _get_tool_suffix_ids(self, tool_messages):
         ]
         if self._is_vlm:
             dummy_messages = prepare_multimodal_messages(dummy_messages)
+            tool_messages = prepare_multimodal_messages(tool_messages)
+
         prefix_ids = self.processing_class.apply_chat_template(
             dummy_messages,
             add_generation_prompt=False,
@@ -1425,66 +1427,18 @@ def _get_tool_suffix_ids(self, tool_messages):
             return_dict=False,
             **self.chat_template_kwargs,
         )
+        full_ids = self.processing_class.apply_chat_template(
+            dummy_messages + tool_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            chat_template=self.chat_template,
+            return_dict=False,
+            **self.chat_template_kwargs,
+        )
         # VLM processors return batched output (list of lists), unbatch for single conversation
         if self._is_vlm:
             prefix_ids = prefix_ids[0]
-
-        # Check if tool messages contain images (multimodal tool responses)
-        tool_images = []
-        for msg in tool_messages:
-            if isinstance(msg.get("content"), list):
-                for part in msg["content"]:
-                    if part["type"] == "image":
-                        tool_images.append(part["image"])
-
-        # Normalize string content in tool messages for VLM processors before either path.
-        # Use copies to avoid mutating the original completions data.
-        if self._is_vlm:
-            tool_messages = [
-                {**msg, "content": [{"type": "text", "text": msg["content"]}]}
-                if isinstance(msg.get("content"), str)
-                else msg
-                for msg in tool_messages
-            ]
-
-        if tool_images and self._is_vlm:
-            # For VLMs with images: use processor.__call__ to get correctly expanded image tokens.
-            # apply_chat_template only inserts a single <|image_pad|> placeholder per image,
-            # but the model needs N tokens per image (based on resolution). The processor's
-            # __call__ handles this expansion.
-            # Use the same tokenization method (processor.__call__) for both prefix and full to
-            # avoid mismatches from different tokenization paths.
-            prefix_text = self.processing_class.apply_chat_template(
-                dummy_messages,
-                add_generation_prompt=False,
-                tokenize=False,
-                chat_template=self.chat_template,
-                **self.chat_template_kwargs,
-            )
-            prefix_ids = self.processing_class(text=prefix_text, return_tensors="pt")["input_ids"][0].tolist()
-            full_text = self.processing_class.apply_chat_template(
-                dummy_messages + tool_messages,
-                add_generation_prompt=True,
-                tokenize=False,
-                chat_template=self.chat_template,
-                **self.chat_template_kwargs,
-            )
-            # We only need input_ids (for suffix token extraction). pixel_values and image_grid_thw
-            # are computed separately in the forward pass via image_processor to avoid mismatches.
-            full_ids = self.processing_class(text=full_text, images=tool_images, return_tensors="pt")["input_ids"][
-                0
-            ].tolist()
-        else:
-            full_ids = self.processing_class.apply_chat_template(
-                dummy_messages + tool_messages,
-                add_generation_prompt=True,
-                tokenize=True,
-                chat_template=self.chat_template,
-                return_dict=False,
-                **self.chat_template_kwargs,
-            )
-            if self._is_vlm:
-                full_ids = full_ids[0]
+            full_ids = full_ids[0]
 
         # Some chat templates (notably Qwen3/Qwen3.5) render "...<|im_end|>\n" after an assistant/tool block.
         # When we compute `suffix_ids` by slicing `full_ids`, we must align the slicing boundary to