---
title: X-Coder-SFT-376k
canonical_url: "https://www.modelscope.cn/datasets/IIGroup/X-Coder-SFT-376k"
md_url: "https://www.modelscope.cn/datasets/IIGroup/X-Coder-SFT-376k.md"
repository: IIGroup/X-Coder-SFT-376k
last_updated: 2026-02-07
license: mit
storage_size: "21 GB"
downloads: 455
stars: 0
---

# X-Coder-SFT-376k

> X-Coder-SFT-376k - IIGroup 在 ModelScope 开源的数据集。X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests

IIGroup/X-Coder-SFT-376k 是 ModelScope 魔搭社区上的数据集，存储大小 21 GB，采用 mit 许可。

- **Repository**: IIGroup/X-Coder-SFT-376k
- **License**: mit
- **Storage size**: 21 GB
- **Downloads**: 455
- **Stars**: 0
- **Last updated**: 2026-02-07

Source: https://www.modelscope.cn/datasets/IIGroup/X-Coder-SFT-376k

---

# X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests

## Dataset Overview

X-Coder-SFT-376k is a large-scale, fully synthetic dataset for advancing competitive programming.
The dataset comprises **4 subsets** with a total of **887,321 synthetic records** across **423,883 unique queries**.
It is designed for supervised fine-tuning and suitbale for cold start to train code reasoning foundations.

X-Coder-SFT-376k is curated by sota reasoning models. 
The query is synthesized by [OpenAI GPT-o3-mini](https://openai.com/zh-Hant/index/openai-o3-mini/), the solutions are generated by [DeepSeek-R1-0528](deepseek-ai/DeepSeek-R1-0528) and [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507).

[Technical Report](https://arxiv.org/abs/2601.06953) - Discover the methodology details behind the Dataset.

[Github Repo](https://github.com/JieWu02/X-Coder) - Access the complete pipeline to generate your own artifacts, and perform SFT & RLVR.


## Data Scaling

![scaling](scaling.png)

Scaling laws on the generated SFT dataset. Left: Performance comparison of on LiveCodebench v5 to examine scaling trend. Right: Performance comparison across scaling
unique tasks and scaling solutions per task.

## Dataset Statistics


<h4 style="margin-bottom: 0px; padding-bottom: 0px;">hybrid</h4>

| Responses | Queries | Percentage |
|-----------|---------|------------|
| 1 | 142,404 | 80.48% |
| 2-7 | 17,329 | 9.79% |
| 8 | 13,968 | 7.89% |
| 9-16 | 3,234 | 1.83% |

#### unique-prompt
- 100% queries with 1 response

#### verified
- 100% queries with exactly 8 responses
- All verified high-quality solutions

<h4 style="margin-bottom: 0px; padding-bottom: 0px;">multiple-solution</h4>

| Responses | Queries | Percentage |
|-----------|---------|------------|
| 1 | 89 | 0.27% |
| 2 | 328 | 0.98% |
| 3 | 1,403 | 4.18% |
| 4 | 2,339 | 6.97% |
| 5 | 4,292 | 12.78% |
| 6 | 5,653 | 16.84% |
| 7 | 6,738 | 20.07% |
| 8 | 12,729 | 37.92% |


<h4 style="margin-bottom: 0px; padding-bottom: 0px;">Record Distribution</h4>

| Split | Records | Unique Queries | Responses/Query | Thinking Tags | Size |
|-------|---------|----------------|-----------------|----------------|------|
| **unique-prompt** | 202,128 | 202,125 | 1.00 | ❌ No | 13.1 GB |
| **multiple-solution** | 218,686 | 33,571 | 6.51 | ✅ Yes | 13.6 GB |
| **hybrid** | 376,491 | 176,935 | 2.13 | ✅ Yes | 24.3 GB |
| **verified** | 90,016 | 11,252 | 8.00 | ✅ Yes | 5.2 GB |


## Dataset Splits

### 1. `hybrid` (Hybrid-SFT-376k)
- **Purpose**: Large-scale SFT training
- **Features**:
  - Mixed dataset with three tiers:
    - 80.48% single-response queries 
    - 7.89% queries with 8 verified responses 
    - 11.63% queries with 2-16 responses 
  - 100% include reasoning process by R1-0528 and Qwen3-235b-a22-2507-thinking
  - Contains the entire `verified` split
  - Partially overlaps with `multiple-solution` (~55%)

### 2. `unique-prompt` (SFT-unique-prompt-202k)
- **Features**:
  - Better query diversity. Each query has exactly 1 response
  - Completely independent from other splits

### 3. `multiple-solution` (SFT-multiple-solution-218k)
- **Purpose**: Training models to generate diverse solutions
- **Features**:
  - Better solution diversity. Most queries with multiple sampled responses (avg 6.51)
  - Response count distribution: 1-8 responses per query
- **Use Cases**: Solution diversity

### 4. `verified` (SFT-verified-90k)
- **Purpose**: High-quality verified solutions
- **Features**:
  - Exactly 8 verified responses per query
  - Subset of `hybrid` split (100% contained)

## Data Format

Each record is a JSON object with the following structure:

```json
{
  "query": "Problem description or coding challenge...",
  "response": "Solution with optional <think>reasoning process</think> and answer..."
}
```


## Dataset Relationships

```
unique-prompt (202k) is completely independent with Other splits

multiple-solution (218k)
            ↓ (~55% overlap)
        hybrid (376k)
            ↓ (100% contained)
        verified (90k)
```

### Overlap:
- `unique-prompt` ∩ others = ∅ (no overlap)
- `multiple-solution` ∩ `hybrid` ≈ 55%
- `verified` ⊂ `hybrid` (100% contained)
- `multiple-solution` ∩ `verified` = ∅

If mixing multiple splits:
- ✅ `unique-prompt` + any other: No deduplication needed
- ⚠️ `multiple-solution` + `hybrid`: Requires deduplication (~55% overlap)
- ⚠️ `verified` + `hybrid`: Use `hybrid` only (verified is fully contained)

## Usage Examples

```python
# Use the comprehensive hybrid split
SFT-376k = load_dataset("IIGroup/X-Coder-SFT-376k", split="hybrid")
```



### 📝 Citation
```
@article{wu2026x,
  title={X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests},
  author={Wu, Jie and Li, Haoling and Zhang, Xin and Guo, Jiani and Luo, Jane and Liu, Steven and Huang, Yangyu and Chu, Ruihang and Li, Scarlett and Yang, Yujiu},
  journal={arXiv preprint arXiv:2601.06953},
  year={2026}
}
```

## License

This dataset is licensed under the MIT license.

---

**Version**: 2026-01-05