[Misc] Rename think_start_str/think_end_str to reasoning_start_str/reasoning_end_str (#38242)

Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com>
(cherry picked from commit cbe7d180)

docs/features/reasoning_outputs.md

@@ -244,12 +244,12 @@ response = client.chat.completions.create(
 
 Some models, such as [Qwen3](https://qwen.readthedocs.io/en/latest/getting_started/quickstart.html#thinking-budget), [DeepSeek](https://www.alibabacloud.com/help/en/model-studio/deep-thinking), and [Nemotron3](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16), support a thinking budget that limits the maximum number of tokens used for reasoning.
 
-Token counting starts from `think_start_str`. Once the reasoning token count reaches the configured `thinking_token_budget`, vLLM forces the model to produce `think_end_str`, effectively terminating the reasoning block.
+Token counting starts from `reasoning_start_str`. Once the reasoning token count reaches the configured `thinking_token_budget`, vLLM forces the model to produce `reasoning_end_str`, effectively terminating the reasoning block.
 
 To use this feature:
 
 - `--reasoning-parser` enables reasoning extraction.
-- `--reasoning-config` defines the reasoning boundary tokens (e.g., `think_start_str`, `think_end_str`).
+- `--reasoning-config` defines the reasoning boundary tokens (e.g., `reasoning_start_str`, `reasoning_end_str`).
 - `thinking_token_budget` (a sampling parameter) sets the per-request reasoning token limit.
 
 If `thinking_token_budget` is not specified, no explicit reasoning limit is applied beyond normal generation constraints such as `max_tokens`.
@@ -258,19 +258,19 @@ If `thinking_token_budget` is not specified, no explicit reasoning limit is appl
 [ReasoningConfig][vllm.config.ReasoningConfig] with the following fields:
 
 | Field                 | Type           | Description                                      |
-|-------------------|----------------|--------------------------------------------------|
-| `think_start_str` | `str \| null`  | String that marks the start of reasoning content |
-| `think_end_str`   | `str \| null`  | String that marks the end of reasoning content   |
+|-----------------------|----------------|--------------------------------------------------|
+| `reasoning_start_str` | `str \| null`  | String that marks the start of reasoning content |
+| `reasoning_end_str`   | `str \| null`  | String that marks the end of reasoning content   |
 
 !!! note
-    `think_end_str` can include a transition phrase before the think end token. For example, setting `think_end_str` to `"I have to give the solution based on the thinking directly now.</think>"` instructs the model to emit that phrase when the budget is exhausted, making the reasoning termination more natural.
+    `reasoning_end_str` can include a transition phrase before the reasoning end token. For example, setting `reasoning_end_str` to `"I have to give the solution based on the reasoning directly now.</think>"` instructs the model to emit that phrase when the budget is exhausted, making the reasoning termination more natural.
 
 ### Online Serving
 
 ```bash
 vllm serve Qwen/Qwen3-0.6B \
     --reasoning-parser qwen3 \
-    --reasoning-config '{"think_start_str": "<think>", "think_end_str": "I have to give the solution based on the thinking directly now.</think>"}'
+    --reasoning-config '{"reasoning_start_str": "<think>", "reasoning_end_str": "I have to give the solution based on the reasoning directly now.</think>"}'
 ```
 
 Then make a request with `thinking_token_budget` to limit the reasoning tokens:
@@ -298,8 +298,8 @@ from vllm.config import ReasoningConfig
 llm = LLM(
     model="Qwen/Qwen3-0.6B",
     reasoning_config=ReasoningConfig(
-        think_start_str="<think>",
-        think_end_str="I have to give the solution based on the thinking directly now.</think>",
+        reasoning_start_str="<think>",
+        reasoning_end_str="I have to give the solution based on the thinking directly now.</think>",
     ),
 )
 

tests/v1/entrypoints/openai/test_thinking_token_budget.py

@@ -20,7 +20,7 @@ def server():
         "--reasoning-parser",
         "qwen3",
         "--reasoning-config",
-        '{"think_start_str": "<think>", "think_end_str": "</think>"}',
+        '{"reasoning_start_str": "<think>", "reasoning_end_str": "</think>"}',
         "--max-model-len",
         "2048",
         "--enforce-eager",

tests/v1/logits_processors/test_correctness.py

@@ -103,8 +103,8 @@ class LogitsProcsRequestParams:
 class MockReasoningConfig:
     """Mock reasoning config for testing ThinkingTokenBudgetLogitsProcessor."""
 
-    think_start_token_ids = [THINK_START_TOKEN_ID]
-    think_end_token_ids = [THINK_END_TOKEN_ID]
+    reasoning_start_token_ids = [THINK_START_TOKEN_ID]
+    reasoning_end_token_ids = [THINK_END_TOKEN_ID]
 
 
 def _generate_fake_sampling_metadata(
@@ -491,7 +491,7 @@ def _thinking_budget_validate(
 
         # Find if thinking has started in output tokens
         thinking_started = False
-        start_tokens = tb_processor.think_start_token_ids
+        start_tokens = tb_processor.reasoning_start_token_ids
 
         if len(start_tokens) > 0:
             for i in range(len(output_tokens) - len(start_tokens) + 1):
@@ -518,7 +518,7 @@ def _thinking_budget_validate(
                     )
 
                 # Validate that only end tokens are allowed
-                end_tokens = tb_processor.think_end_token_ids
+                end_tokens = tb_processor.reasoning_end_token_ids
                 if len(end_tokens) > 0:
                     expected_end_token_id = end_tokens[
                         min(state["end_count"], len(end_tokens) - 1)

vllm/config/reasoning.py

@@ -12,7 +12,7 @@ from vllm.tokenizers import cached_tokenizer_from_config
 class ReasoningConfig:
     """Configuration for reasoning models.
 
-    Set `think_start_str` and `think_end_str` to the strings that delimit
+    Set `reasoning_start_str` and `reasoning_end_str` to the strings that delimit
     the reasoning block (e.g. `"<think>"` and `"</think>"`).  The
     corresponding token IDs are derived automatically via
     `initialize_token_ids` and are not intended to be set directly.
@@ -20,53 +20,55 @@ class ReasoningConfig:
 
     # NOTE: These parameters are temporary, the intent is to derive them
     # automatically from the reasoning parser in a future version.
-    think_start_str: str = "<think>"
+    reasoning_start_str: str = "<think>"
     """String that indicates the start of reasoning."""
-    think_end_str: str = "</think>"
+    reasoning_end_str: str = "</think>"
     """String that indicates the end of reasoning content."""
 
-    _think_start_token_ids: list[int] | None = field(
+    _reasoning_start_token_ids: list[int] | None = field(
         default=None, init=False, repr=False
     )
-    """Private backing field for `think_start_token_ids`. Set by
+    """Private backing field for `reasoning_start_token_ids`. Set by
     `initialize_token_ids`. Not intended to be configured directly."""
-    _think_end_token_ids: list[int] | None = field(default=None, init=False, repr=False)
-    """Private backing field for `think_end_token_ids`. Set by
+    _reasoning_end_token_ids: list[int] | None = field(
+        default=None, init=False, repr=False
+    )
+    """Private backing field for `reasoning_end_token_ids`. Set by
     `initialize_token_ids`. Not intended to be configured directly."""
 
     @property
-    def think_start_token_ids(self) -> list[int] | None:
-        """Token IDs derived from `think_start_str`. Set automatically by
+    def reasoning_start_token_ids(self) -> list[int] | None:
+        """Token IDs derived from `reasoning_start_str`. Set automatically by
         `initialize_token_ids`. Not intended to be configured directly."""
-        return self._think_start_token_ids
+        return self._reasoning_start_token_ids
 
     @property
-    def think_end_token_ids(self) -> list[int] | None:
-        """Token IDs derived from `think_end_str`. Set automatically by
+    def reasoning_end_token_ids(self) -> list[int] | None:
+        """Token IDs derived from `reasoning_end_str`. Set automatically by
         `initialize_token_ids`. Not intended to be configured directly."""
-        return self._think_end_token_ids
+        return self._reasoning_end_token_ids
 
     def initialize_token_ids(self, model_config: ModelConfig) -> None:
         """Initialize reasoning token IDs from strings using the tokenizer."""
         if (
-            self._think_start_token_ids is not None
-            and self._think_end_token_ids is not None
+            self._reasoning_start_token_ids is not None
+            and self._reasoning_end_token_ids is not None
         ):
             return
 
         tokenizer = cached_tokenizer_from_config(model_config=model_config)
 
-        self._think_start_token_ids = tokenizer.encode(
-            self.think_start_str, add_special_tokens=False
+        self._reasoning_start_token_ids = tokenizer.encode(
+            self.reasoning_start_str, add_special_tokens=False
         )
-        self._think_end_token_ids = tokenizer.encode(
-            self.think_end_str, add_special_tokens=False
+        self._reasoning_end_token_ids = tokenizer.encode(
+            self.reasoning_end_str, add_special_tokens=False
         )
 
-        if not self._think_start_token_ids or not self._think_end_token_ids:
+        if not self._reasoning_start_token_ids or not self._reasoning_end_token_ids:
             raise ValueError(
                 f"ReasoningConfig: failed to tokenize reasoning strings: "
-                f"think_start_str='{self.think_start_str}', "
-                f"think_end_str='{self.think_end_str}'. "
+                f"reasoning_start_str='{self.reasoning_start_str}', "
+                f"reasoning_end_str='{self.reasoning_end_str}'. "
                 "Ensure the strings are valid tokens in the model's vocabulary."
             )

vllm/v1/sample/logits_processor/builtin.py

@@ -303,10 +303,12 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
         # Check if thinking is enabled
         self.is_enabled = reasoning_config is not None
 
-        self.think_start_token_ids = getattr(
-            reasoning_config, "think_start_token_ids", []
+        self.reasoning_start_token_ids = getattr(
+            reasoning_config, "reasoning_start_token_ids", []
+        )
+        self.reasoning_end_token_ids = getattr(
+            reasoning_config, "reasoning_end_token_ids", []
         )
-        self.think_end_token_ids = getattr(reasoning_config, "think_end_token_ids", [])
 
         self.pin_memory = is_pin_memory
         self.device = device
@@ -357,15 +359,15 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
             think_count = 0
         else:
             last_start = self._find_last_sequence_index(
-                prompt_tok_ids, self.think_start_token_ids
+                prompt_tok_ids, self.reasoning_start_token_ids
             )
             last_end = self._find_last_sequence_index(
-                prompt_tok_ids, self.think_end_token_ids
+                prompt_tok_ids, self.reasoning_end_token_ids
             )
             in_think = last_start > last_end
             if in_think:
                 think_count = len(prompt_tok_ids) - (
-                    last_start + len(self.think_start_token_ids)
+                    last_start + len(self.reasoning_start_token_ids)
                 )
             else:
                 think_count = 0
@@ -405,8 +407,8 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
         state["prev_output_length"] = current_length
 
         # Check if new tokens contain think start or end sequences
-        start_len = len(self.think_start_token_ids)
-        end_len = len(self.think_end_token_ids)
+        start_len = len(self.reasoning_start_token_ids)
+        end_len = len(self.reasoning_end_token_ids)
 
         # Look for think sequences in recent tokens (including boundary)
         # Check overlapping regions where sequences might span boundaries
@@ -415,10 +417,10 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
 
         # Find any think start/end sequences in recent tokens
         recent_start_pos = self._find_last_sequence_index(
-            recent_tokens, self.think_start_token_ids
+            recent_tokens, self.reasoning_start_token_ids
         )
         recent_end_pos = self._find_last_sequence_index(
-            recent_tokens, self.think_end_token_ids
+            recent_tokens, self.reasoning_end_token_ids
         )
 
         # Update state based on recent sequences
@@ -469,7 +471,7 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
         else:
             # In end mode
             state["end_count"] += 1
-            if state["end_count"] >= len(self.think_end_token_ids):
+            if state["end_count"] >= len(self.reasoning_end_token_ids):
                 state.update(
                     {
                         "in_end": False,
@@ -530,7 +532,9 @@ class ThinkingTokenBudgetLogitsProcessor(LogitsProcessor):
             state = self._state.get(i)
             if state and state["in_end"]:
                 self.mask[i] = True
-                self.force_token_ids[i] = self.think_end_token_ids[state["end_count"]]
+                self.force_token_ids[i] = self.reasoning_end_token_ids[
+                    state["end_count"]
+                ]
 
         # Check in CPU first not to sync with GPU
         has_active_thinking = any(