Improve model card: Add pipeline tag, library name, paper, and code links

README.md

@@ -1,19 +1,28 @@
 ---
-license: mit
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
 datasets:
 - AceSearcher/Search-SFT
 - AceSearcher/Search-RFT-Prompts
 language:
 - en
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
 ---
-## 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.
+
+# AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
+
+This repository contains the checkpoint for **AceSearcher-8B**, a large language model developed in the paper [AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play](https://huggingface.co/papers/2509.24193). It uses `Llama-3.1-8B` as its backbone.
+
+For the official code and more details, visit the [GitHub repository](https://github.com/ritaranx/AceSearcher/).
+
+## Paper Abstract
+Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-play framework that trains a single large language model (LLM) to alternate between two roles: a decomposer that breaks down complex queries and a solver that integrates retrieved contexts for answer generation. AceSearcher couples supervised fine-tuning on a diverse mixture of search, reasoning, and decomposition tasks with reinforcement fine-tuning optimized for final answer accuracy, eliminating the need for intermediate annotations. Extensive experiments on three reasoning-intensive tasks across 10 datasets show that AceSearcher outperforms state-of-the-art baselines, achieving an average exact match improvement of 7.6%. Remarkably, on document-level finance reasoning tasks, AceSearcher-32B matches the performance of the DeepSeek-V3 model using less than 5% of its parameters. Even at smaller scales (1.5B and 8B), AceSearcher often surpasses existing search-augmented LLMs with up to 9x more parameters, highlighting its exceptional efficiency and effectiveness in tackling complex reasoning tasks. Our code will be published at this https URL and this https URL .
 
 ## Model Usage
 For question decomposition on QA tasks: 
-```
+```python
 from vllm import LLM, SamplingParams
 model_path = "AceSearcher/AceSearcher-8B"
 
@@ -41,7 +50,7 @@ generated_text = outputs[0].outputs[0].text
 ```
 
 For question decomposition on fact verification tasks: 
-```
+```python
 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:"""
@@ -64,7 +73,7 @@ generated_text = outputs[0].outputs[0].text
 ```
 
 For question answering for subquestions:
-```
+```python
 prompt = f"""You have the following context passages:
 {context_text}
 
@@ -73,7 +82,7 @@ If no answer is found in the context, use your own knowledge. Your answer needs
 ```
 
 For fact verification tasks for subquestions:
-```
+```python
 prompt = f"""You have the following context passages:
 {context_text}
 
@@ -83,7 +92,7 @@ Please only output Yes or No and do not give any explanation."""
 ```
 
 For question answering to generate the final answer:
-```
+```python
 prompt = f"""You have the following passages:
 {passages}
 
@@ -96,7 +105,7 @@ Wrap your answer with <answer> and </answer> tags."""
 ```
 
 For fact verification tasks to generate the final answer:
-```
+```python
 prompt = f"""You have the following passages:
 {passages}
 
@@ -108,15 +117,36 @@ 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."""
+```python
+decompose_prompt = """You have the following passages and table:\
+Passages:\
+{passage}\
+Please 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."""
+qa_prompt = """You have the following passages and table:\
+Passages:\
+{passage}\
+For the question '{question}', here is a referenced breakdown:\
+{decompose}.\
+\
+Write 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"
+context_text = "\
+|||December 31,||\
+||Useful Life|2019|2018|\
+|Computer equipment and software|3 \\u2013 5 years|$57,474|$52,055|\
+|Furniture and fixtures|7 years|6,096|4,367|\
+|Leasehold improvements|2 \\u2013 6 years|22,800|9,987|\
+|Renovation in progress|n/a|8|1,984|\
+|Build-to-suit property|25 years|\\u2014|51,058|\
+|Total property and equipment, gross||86,378|119,451|\
+|Less: accumulated depreciation and amortization||(49,852)|(42,197)|\
+|Total property and equipment, net||$36,526|$77,254|\
+ 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.\
+"
 
 decompose_prompt = decompose_prompt.replace("{passage}" , context_text)
 decompose_prompt = decompose_prompt.replace("{question}", question)
@@ -135,7 +165,7 @@ 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!
 
-```
+```bibtex
 @inproceedings{
 xu2025acesearcher,
 title={AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play},