Multi-turn Rollout Supportļƒ

Basic Configurationļƒ

To enable multi-turn rollout, make sure to configure the following fields in your rollout configuration:

actor_rollout_ref:
    rollout:
        multi_turn: True
        name: "sglang"

These configuration activates the sglang engine for multi-turn interaction during rollout.

Custom Tool Configurationļƒ

For custom environment interaction tools, you can implement your own tools based on verl.tools.base_tool.BaseTool. Then, specify your tool configurations in a YAML file:

tools:
  - class_name: ""
    config: {}
    tool_schema:

You may refer to GSM8KTool_example_configuration, which is one example of the tool configurations. Its implementation can be found in gsm8k_tool.py.

Finally, set the tools_config_file in your rollout config:

actor_rollout_ref:
    rollout:
        tool_kwargs:
            tools_config_file: <path_to_tool_yaml_file>

This allows integration of customized tool behaviors during actor rollout steps.

If you want rollout with simulated interaction, you can set the interaction_config_file in your rollout config: .. code-block:: yaml

interaction:

  • class_name: ā€œā€ config: {}

actor_rollout_ref:
    rollout:
        interaction_config_file: <path_to_interaction_yaml_file>

Multi-turn Tokenizationļƒ

Tokenizing multi-turn rollouts poses a challenge: after applying the chat template and tokenizing the full message list, itā€™s hard to identify which tokens belong to assistant messages. Since the token list is flat, it lacks direct alignment with the message roles.

To address this, we adopt a delta-based tokenization strategy. Each time the LLM generates a new message, we:

  1. Apply the chat template to all prior messages (messages[:i]).

  2. Apply the chat template again including the latest message (messages[:i+1]).

  3. Tokenize only the delta between these two serialized message strings.

This ensures that only tokens generated by the assistant are included in the loss mask.

# When using tokenizer
# Exclude the assistant prompt (e.g., "<|im_start|>assistant") from the loss by setting add_generation_prompt=True
prev = tokenizer.apply_chat_template(messages[:i], add_generation_prompt=True, tokenize=False)
curr = tokenizer.apply_chat_template(messages[:i+1], add_generation_prompt=False, tokenize=False)
token_ids += tokenizer.encode(curr[len(prev):], add_special_tokens=False)
loss_mask += [1] * len(token_ids)  # Mask only the new assistant tokens

# When using processor
# Exclude the assistant prompt (e.g., "<|im_start|>assistant") from the loss by setting add_generation_prompt=True
prev = processor.apply_chat_template(messages[:i], add_generation_prompt=True, tokenize=False)
prev_model_inputs = processor(text=prev, images=images, videos=videos, return_tensors="pt")[0].tolist()
curr = processor.apply_chat_template(messages[:i+1], add_generation_prompt=False, tokenize=False)
curr_model_inputs = processor(text=curr, images=images, videos=videos, return_tensors="pt")[0].tolist()
token_ids += curr_model_inputs["input_ids"][len(prev_model_inputs["input_ids"]):]
loss_mask += [1] * len(token_ids)  # Mask only the new assistant tokens

While weā€™ve validated this produces consistent results with full message tokenization, future modelsā€™ chat template could break compatibility. To guard against silent inconsistencies, we compare the delta-based tokenization with full-tokenization results by default at the end of each rollout.

If you see the following warning, you can check the mismatched substring in the log:

Inconsistent training and inference tokenization detected. This may lead to unexpected behavior during training. Please review your chat template to determine if this is intentional. For more information, refer to the multiturn README.md.

The tokenization sanity check mode can be configured using the actor_rollout_ref.rollout.multi_turn.tokenization_sanity_check_mode parameter, which accepts the following values:

  • strict (default): Performs strict comparison between delta-based and full tokenization results, raising warnings for any differences.

  • ignore_strippable: Ignores differences in whitespace characters (\n, \t, \r, spaces) while still checking for meaningful text mismatches. This is useful when debugging chat template issues where whitespace variations are expected and acceptable.

  • off: Completely disables the tokenization sanity check. Only use this if you have thoroughly validated that tokenization discrepancies are expected and wonā€™t impact training.

Example configuration:

actor_rollout_ref:
    rollout:
        multi_turn:
            tokenization_sanity_check_mode: "ignore_strippable"  # Choose from: "strict", "ignore_strippable", "off"

Special Casesļƒ

Some models (e.g., Qwen/QwQ-32B and Qwen3 series) remove internal reasoning content during chat template rendering. As a result, the message content can vary across turns, making the delta-based tokenization inaccurate.

For example, for the following conversation:

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is 2 + 2?"},
    {"role": "assistant", "content": "<think>user asked about a simple math question.</think> 2 + 2 = 4."},
    {"role": "user", "content": "Explain why."},
    {"role": "assistant", "content": "<think>user wants to know the reasoning behind the answer. Search for a good explanation</think>",
     "tool_calls": [{"id": "tool1", "type": "search", "arguments": {"query": "Why is 2 + 2 = 4?"}}]},
    {"role": "tool", "content": "The sum of two and two is four because it is a basic arithmetic operation."},
    {"role": "assistant", "content": "<think>The tool provided a good explanation.</think>The sum of two and two is four because it is a basic arithmetic operation."}
]

  1. Qwen/QwQ-32B will remove all reasoning content except the last assistant message after applying the chat template.

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2 + 2?<|im_end|>
<|im_start|>assistant
 2 + 2 = 4.<|im_end|>
<|im_start|>user
Explain why.<|im_end|>
<|im_start|>assistant
<tool_call>
{"name": "", "arguments": {"query": "Why is 2 + 2 = 4?"}}
</tool_call><|im_end|>
<|im_start|>user
<tool_response>
The sum of two and two is four because it is a basic arithmetic operation.
</tool_response><|im_end|>
<|im_start|>assistant
<think>The tool provided a good explanation.</think> The sum of two and two is four because it is a basic arithmetic operation.<|im_end|>

  1. Qwen3 series will remove all reasoning content before the last user message.

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2 + 2?<|im_end|>
<|im_start|>assistant
 2 + 2 = 4.<|im_end|>
<|im_start|>user
Explain why.<|im_end|>
<|im_start|>assistant
<think>
user wants to know the reasoning behind the answer. Search for a good explanation
</think>

<tool_call>
{"name": "", "arguments": {"query": "Why is 2 + 2 = 4?"}}
</tool_call><|im_end|>
<|im_start|>user
<tool_response>
The sum of two and two is four because it is a basic arithmetic operation.
</tool_response><|im_end|>
<|im_start|>assistant
<think>
The tool provided a good explanation.
</think>

The sum of two and two is four because it is a basic arithmetic operation.<|im_end|>

To handle this, we fall back to a fixed base conversation containing only a single system and user message. Since this base doesnā€™t include assistant messages or reasoning content, it remains consistent across turns.

BASE_CHAT_HISTORY = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "I am a user."}
]
prev = tokenizer.apply_chat_template(BASE_CHAT_HISTORY, add_generation_prompt=True, tokenize=False)
curr = tokenizer.apply_chat_template([*BASE_CHAT_HISTORY, messages[i]], add_generation_prompt=False, tokenize=False)
token_ids += tokenizer.encode(curr[len(prev):], add_special_tokens=False)
loss_mask += [1] * len(token_ids)

This method works well for Qwen3 series. However, Qwen/QwQ-32B currently has a bug in its chat template. A fix has been proposed but not yet adopted. Until then, use the following command to download the fixed model revision:

pip install huggingface_hub
huggingface-cli download Qwen/QwQ-32B --revision refs/pr/81

Discrepancy Between Training and Inference Templatesļƒ

Although the above approach fixes the delta mismatch issue, the removal of reasoning content in the inference-time chat template introduces a new discrepancy: training uses the full reasoning content, while inference does not.

This mismatch can affect model performance in unpredictable ways. To avoid it, we default to using the full response (including reasoning) for both training and rollout.

However, this approach comes with trade-offs:

  1. Long reasoning contents can easily exceed the modelā€™s context window, especially in multi-turn rollout.

  2. Thereā€™s a mismatch between rollout and production environment nowā€”models will not have reasoning content from past turns if you use the default chat template in production.

We are still evaluating the impact of these issues. If you experience context length problems or prefer rollouts that match production (i.e., exclude reasoning), you can enable:

actor_rollout_ref.rollout.multi_turn.use_inference_chat_template = True

GSM8K Multi-turn Training Performanceļƒ

See the training performance of multi-turn rollout on the GSM8K task HERE.

Interaction Systemļƒ

For dynamic conversational feedback during RL training, see:

Search Tool Integrationļƒ