---
title: AceCode-89K
canonical_url: "https://www.modelscope.cn/datasets/TIGER-Lab/AceCode-89K"
md_url: "https://www.modelscope.cn/datasets/TIGER-Lab/AceCode-89K.md"
repository: TIGER-Lab/AceCode-89K
last_updated: 2025-02-09
license: "Apache License 2.0"
storage_size: "967 MB"
downloads: 1449
stars: 1
---

# AceCode-89K

> AceCode-89K - TIGER-Lab 在 ModelScope 开源的数据集。Paper | Github | AceCode-87K | AceCodePair-300K | RM/RL Models

TIGER-Lab/AceCode-89K 是 ModelScope 魔搭社区上的数据集，存储大小 967 MB，采用 Apache License 2.0 许可。

- **Repository**: TIGER-Lab/AceCode-89K
- **License**: Apache License 2.0
- **Storage size**: 967 MB
- **Downloads**: 1449
- **Stars**: 1
- **Last updated**: 2025-02-09

Source: https://www.modelscope.cn/datasets/TIGER-Lab/AceCode-89K

---

# 🂡 AceCode-87K

[Paper](https://arxiv.org/abs/2502.01718) | 
[Github](https://github.com/TIGER-AI-Lab/AceCoder) |
[AceCode-87K](https://huggingface.co/datasets/TIGER-Lab/AceCode-87K) |
[AceCodePair-300K](https://huggingface.co/datasets/TIGER-Lab/AceCodePair-300K) |
[RM/RL Models](https://huggingface.co/collections/TIGER-Lab/acecoder-67a16011a6c7d65cad529eba)

We introduce AceCoder, the first work to propose a fully automated pipeline for synthesizing large-scale reliable tests used for the reward model training and reinforcement learning in the coding scenario. To do this, we curated the dataset AceCode-87K, where we start from a seed code dataset and prompt powerful LLMs to "imagine" proper test cases for the coding question and filter the noisy ones. We sample inferences from existing coder models and compute their pass rate as the reliable and verifiable rewards for both training the reward model and conducting the reinforcement learning for coder LLM.

- **This dataset is the official AceCodeRM-87K after the test case filtering**. 
- Each question in the dataset is rewritten by GPT-4o-mini along with an average of **16** cleaned test cases.

![https://tiger-ai-lab.github.io/AceCoder/static/images/ac_overview.png](https://tiger-ai-lab.github.io/AceCoder/static/images/ac_overview.png)

## Data Formats

- `Id` (str): Unique identifier for each question
- `source` (str): which dataset
- `question` (str): the question
- `test_cases` (List[str]): test cases for the question
- `inferences` (List[dict]):
    - `model_name` (str): the model name
    - `completion_id` (int): the completion id
    - `completion` (str): the completion
    - `pass_rate` (float): the pass rate
    - `test_results` (List[int]): the test results
- `context_messages` (List[List[dict]]): context messages for the question
    - `content` (str): the content of the message
    - `role` (str): the role of the message
    

## Usage
- **Direct use**
```python
import datasets
dataset = datasets.load_dataset("TIGER-Lab/AceCode-87K", split='train')
```

- **Use for RL tuning**: This dataset can be directly used for RL tuning with OpenRLHF codes, where you should set the `context_messages` as the key.

## Citation
```bibtex
@article{AceCoder,
    title={AceCoder: Acing Coder RL via Automated Test-Case Synthesis},
    author={Zeng, Huaye and Jiang, Dongfu and Wang, Haozhe and Nie, Ping and Chen, Xiaotong and Chen, Wenhu},
    journal={ArXiv},
    year={2025},
    volume={abs/2207.01780}
}
```