AceSearcher-32B

Introduction

Here is the checkpoint used in the paper AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play. It uses Qwen-2.5-Instruct-32B as the backbone.

Model Usage

For question decomposition on QA tasks:

from vllm import LLM, SamplingParams
model_path = "AceSearcher/AceSearcher-32B"

llm = LLM(model=model_path, tensor_parallel_size=2, gpu_memory_utilization=0.85, trust_remote_code=True)

prompt_plan_qa = """Please break down the question "{question}" into multiple specific sub-questions that address individual components of the original question. 
Mark each sub-question with ### at the beginning.  If you need to refer to answers from earlier sub-questions, use #1, #2, etc., to indicate the corresponding answers.
Decomposed Question:"""

prompt_qa = prompt_plan_qa.replace("{question}", question)

prompt = [
    {"role": "user", "content": prompt_qa.strip()}
] 

text = tokenizer.apply_chat_template(
    prompt,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

outputs = llm.generate([text], sampling_params)
generated_text = outputs[0].outputs[0].text

For question decomposition on fact verification tasks:

prompt_plan_claim = """Please break down the claim "{claim}" into multiple smaller sub-claims that each focus on a specific component of the original statement, making it easier for a model to verify.
Begin each sub-claim with ###. If needed, refer to answers from earlier sub-claims using #1, #2, etc.
Decomposed claim:"""

prompt_plan_claim = prompt_plan_claim.replace("{question}", question)

prompt = [
    {"role": "user", "content": prompt_plan_claim.strip()}
] 

text = tokenizer.apply_chat_template(
    prompt,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

outputs = llm.generate([text], sampling_params)
generated_text = outputs[0].outputs[0].text

For question answering for subquestions:

prompt = f"""You have the following context passages:
{context_text}

Please answer the question '{sub_q}' with a short span using the context as reference.
If no answer is found in the context, use your own knowledge. Your answer needs to be as short as possible."""

For fact verification tasks for subquestions:

prompt = f"""You have the following context passages:
{context_text}

Please verify whether the claim '{sub_q}' is correct using the context as reference. 
If no answer is found in the context, use your own knowledge.
Please only output Yes or No and do not give any explanation."""

For question answering to generate the final answer:

prompt = f"""You have the following passages:
{passages}

You are also given some subquestions and their answers:
{sub_answer_text}

Please answer the question '{original_question}' with {final_prompt} using the documents and subquestions as reference.
Make sure your response is grounded in documents and provides clear reasoning followed by a concise conclusion. If no relevant information is found, use your own knowledge. 
Wrap your answer with <answer> and </answer> tags."""

For fact verification tasks to generate the final answer:

prompt = f"""You have the following passages:
{passages}

You are given some subquestions and their answers:
{sub_answer_text}

Please verify the correctness of the claim: '{original_question}' using the subquestions as reference. Please provide a concise and clear reasoning followed by a concise conclusion. Your answer should be Yes or No only. 
Wrap your answer with <answer> and </answer> tags."""

For Decomposition for document-level financial reasoning tasks:

decompose_prompt = """You have the following passages and table:\nPassages:\n{passage}\nPlease break down the question '{question}' into multiple specific sub-questions that address individual components of the original question, with the table and passages as the reference. Use ### to mark the start of each sub-question."""

qa_prompt = """You have the following passages and table:\nPassages:\n{passage}\nFor the question '{question}', here is a referenced breakdown:\n{decompose}.\n\nWrite a Python program to solve the question. Store the final result in the variable ans."""


question = "What would the change in furniture and fixtures between 2018 and 2019 be if furniture and fixtures were $5,000 thousand in 2018 instead? (in thousand)"

context_text = "\n|||December 31,||\n||Useful Life|2019|2018|\n|Computer equipment and software|3 \u2013 5 years|$57,474|$52,055|\n|Furniture and fixtures|7 years|6,096|4,367|\n|Leasehold improvements|2 \u2013 6 years|22,800|9,987|\n|Renovation in progress|n/a|8|1,984|\n|Build-to-suit property|25 years|\u2014|51,058|\n|Total property and equipment, gross||86,378|119,451|\n|Less: accumulated depreciation and amortization||(49,852)|(42,197)|\n|Total property and equipment, net||$36,526|$77,254|\n 7. OTHER BALANCE SHEET AMOUNTS The components of property and equipment, net is as follows (in thousands): Depreciation expense for the years ended December 31, 2019, 2018, and 2017 was $11.8 million, $10.2 million, and $10.3 million, respectively.\n"

decompose_prompt = decompose_prompt.replace("{passage}" , context_text)
decompose_prompt = decompose_prompt.replace("{question}", question)
message = [{"role": "user", "content": decompose_prompt.strip()}]
prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
generated_text = llm.generate(prompt, sampling_params)[0].outputs[0].text

qa_prompt = qa_prompt.replace("{passage}", context_text)
qa_prompt = qa_prompt.replace("{question}", question)
qa_prompt = qa_prompt.replace("{decompose}", generated_text)
message = [{"role": "user", "content": qa_prompt.strip()}]
prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
output = llm.generate(prompt, sampling_params)[0].outputs[0].text

Citation

If you find our paper or models helpful, please consider cite as follows. Thank you!

@inproceedings{
xu2025acesearcher,
title={AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play},
author={Ran Xu and Yuchen Zhuang and Zihan Dong and Ruiyu Wang and Yue Yu and Joyce C. Ho and Linjun Zhang and Haoyu Wang and Wenqi Shi and Carl Yang },
booktitle={the 39th Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=jSgCM0uZn3}
}