---
title: AReaL-RL-Data
canonical_url: "https://www.modelscope.cn/datasets/inclusionAI/AReaL-RL-Data"
md_url: "https://www.modelscope.cn/datasets/inclusionAI/AReaL-RL-Data.md"
repository: inclusionAI/AReaL-RL-Data
last_updated: 2026-08-31
license: apache-2.0
storage_size: "68 MB"
downloads: 215
stars: 0
---

# AReaL-RL-Data

> AReaL-RL-Data - inclusionAI 在 ModelScope 开源的数据集。AReaL: Ant Reasoning RL

inclusionAI/AReaL-RL-Data 是 ModelScope 魔搭社区上的数据集，存储大小 68 MB，采用 apache-2.0 许可。

- **Repository**: inclusionAI/AReaL-RL-Data
- **License**: apache-2.0
- **Storage size**: 68 MB
- **Downloads**: 215
- **Stars**: 0
- **Last updated**: 2026-08-31

Source: https://www.modelscope.cn/datasets/inclusionAI/AReaL-RL-Data

---

# AReaL: Ant Reasoning RL

**A fully open-sourced and inclusive RL project for large reasoning models**

AReaL (Ant Reasoning RL) is an open-source and efficient reinforcement learning system developed at **the RL Lab, Ant Research**. AReaL inherits and adapts the Open-Source Project [ReaLHF](https://github.com/openpsi-project/ReaLHF) for training Large Reasoning Models (LRMs) that everyone can reproduce and contribute to. AReaL is part of our efforts from Ant Research to develop tools and systems for a fully open and inclusive AGI world.

**AReaL Highlights**  
-  🛠️ **Open & Reproducible**: We will continuously release *all code, datasets, and training recipes* for training LRMs --- no hidden secrects or proprietary barriers.  
-  🚀 **Scalable Performance**: AReaL can seamlessly adapt to different computational resource settings, ranging from 1 single node to hundreds of GPUs.
-  🌍 **Community-Driven AGI**: With a fully open-source commitment, we hope our efforts can benefit the entire community to accelerate AGI research.

**Github URL**: https://github.com/inclusionAI/AReaL 

---


# Content

We release our training dataset in this repository.
The RL training dataset consists of 40k high-quality mathematical reasoning tasks
released by [DeepScaleR](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset).
We are also actively developing better datasets suitable for training stronger and larger models in future releases.

+ `data/id2info.json`: The solutions to each question indexed by query ID. Used for computing rewards during training.
+ `prompts_for_r1_distilled.jsonl`: The dataset for training the [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) model.
+ `prompts_for_zero.jsonl`: The dataset for training the R1-Zero-style model from [`Qwen2.5-7B`](https://huggingface.co/Qwen/Qwen2.5-7B).

To reproduce our results, please refer to the [step-by-step guideline](https://github.com/inclusionAI/AReaL/examples/) on GitHub.

```bash
# Download the dataset
DATA_PATH=/storage/datasets/
cd $DATA_PATH
wget https://huggingface.co/datasets/inclusionAI/AReaL-RL-Data/resolve/main/data/prompts_for_r1_distilled.jsonl?download=true
wget https://huggingface.co/datasets/inclusionAI/AReaL-RL-Data/resolve/main/data/id2info.json?download=true

# Training in a Ray cluster with 16 nodes

# stage 1
MODEL_PATH=${path_to_DeepSeek-R1-Distill-Qwen-1.5B}
bash ./examples/train_1.5B_n16_on_ray.sh $MODEL_PATH $DATA_PATH 8192 

# stage 2
MODEL_PATH=${model_path_from_stage_1}
bash ./examples/train_1.5B_n16_on_ray.sh $MODEL_PATH $DATA_PATH 16384

# stage 3
MODEL_PATH=${model_path_from_stage_2}
bash ./examples/train_1.5B_n16_on_ray.sh $MODEL_PATH $DATA_PATH 24000

```