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[sglang] Fixing issues in examples/sglang_multiturn/search_r1_like/run_qwen2.5-3b_instruct_search_multiturn.sh #5114

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[sglang] Fixing issues in examples/sglang_multiturn/search_r1_like/run_qwen2.5-3b_instruct_search_multiturn.sh #5114

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9852145
Update multi-turn settings in run_qwen2.5 script
NLPJCL Jan 29, 2026
61121a7
Update search_multiturn_grpo.yaml
NLPJCL Jan 29, 2026
7ad8985
Update preprocess_search_r1_dataset.py
NLPJCL Jan 29, 2026
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Update preprocess_search_r1_dataset.py 
To adapt to the tool call format of Verl's AgentLoop, modify the prompt as follows:
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@NLPJCL
NLPJCL authored Jan 29, 2026
commit 7ad89859e1eb74e3e4e62eb4205e32032f838503
19 changes: 16 additions & 3 deletions examples/data_preprocess/preprocess_search_r1_dataset.py
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Expand Up @@ -30,10 +30,22 @@

# Configuration constants
DEFAULT_SYSTEM_CONTENT = "You are a helpful and harmless assistant."
# DEFAULT_USER_CONTENT_PREFIX = (
# "Answer the given question. You must conduct reasoning inside <think> and </think> "
# "first every time you get new information. After reasoning, if you find you lack "
# "some knowledge, you can call a search engine by <tool_call> query </tool_call> "
# "and it will return the top searched results between <tool_response> and "
# "</tool_response>. You can search as many times as your want. If you find no "
# "further external knowledge needed, you can directly provide the answer inside "
# "<answer> and </answer>, without detailed illustrations. For example, "
# "<answer> Beijing </answer>. Question: "
# )
#To adapt to the tool call format of Verl's AgentLoop, modify the prompt as follows:
DEFAULT_USER_CONTENT_PREFIX = (
"Answer the given question. You must conduct reasoning inside <think> and </think> "
"first every time you get new information. After reasoning, if you find you lack "
"some knowledge, you can call a search engine by <tool_call> query </tool_call> "
"Answer the given question. You must conduct step by step reasoning "
"first every time you get new information. If you need external information, "
"call the search tool by returning a JSON object inside <tool_call> tags. "
"For example: <tool_call>{\"name\": \"search\", \"arguments\": {\"query\": \"your query\"}}</tool_call>. "
"and it will return the top searched results between <tool_response> and "
"</tool_response>. You can search as many times as your want. If you find no "
"further external knowledge needed, you can directly provide the answer inside "
Expand Down Expand Up @@ -176,3 +188,4 @@ def apply_process_row(row, split_name=split):
user_content_prefix = DEFAULT_USER_CONTENT_PREFIX

main()