Create README.md

README.md

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+---
+license: mit
+datasets:
+- AceSearcher/Search-SFT
+- AceSearcher/Search-RFT-Prompts
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+---
+## Introduction
+Here is the checkpoint used in the paper **AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play**. It uses `Llama-3.1-8B` as the backbone.
+
+## Model Usage
+For question decomposition on QA tasks: 
+```
+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}
+}
+```