---
title: X-Coder-RL-Qwen3-8B
canonical_url: "https://www.modelscope.ai/models/IIGroup/X-Coder-RL-Qwen3-8B"
md_url: "https://www.modelscope.ai/models/IIGroup/X-Coder-RL-Qwen3-8B.md"
repository: IIGroup/X-Coder-RL-Qwen3-8B
last_updated: 2026-02-09
license: apache-2.0
model_type:
  - qwen3
architectures:
  - Qwen3ForCausalLM
base_model:
  - IIGroup/X-Coder-SFT-Qwen3-8B
base_model_relation: finetune
parameters: 8.2B
tensor_type:
  - BF16
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 142
stars: 0
tags:
  - code
  - rl
  - competitive-programming
---

# X-Coder-RL-Qwen3-8B

> X-Coder-RL-Qwen3-8B - An open-source model by IIGroup on ModelScope. X-Coder-RL-Qwen3-8B is a code reasoning foundation model trained with RLVR on fully synthetic rl data, achieving strong performance on competitive programming.

IIGroup/X-Coder-RL-Qwen3-8B is a 8.2B-parameter machine learning model on ModelScope. licensed under apache-2.0. derived from IIGroup/X-Coder-SFT-Qwen3-8B.

- **Repository**: IIGroup/X-Coder-RL-Qwen3-8B
- **License**: apache-2.0
- **Parameters**: 8.2B
- **Base model**: IIGroup/X-Coder-SFT-Qwen3-8B
- **Tags**: code, rl, competitive-programming
- **Downloads**: 142
- **Stars**: 0
- **Last updated**: 2026-02-09

Source: https://www.modelscope.ai/models/IIGroup/X-Coder-RL-Qwen3-8B

---

# X-Coder-RL-Qwen3-8B

X-Coder-RL-Qwen3-8B is a code reasoning foundation model trained with RLVR on fully synthetic rl data, achieving strong performance on competitive programming.

## Model Description

- **Base Model**: [IIGroup/X-Coder-SFT-Qwen3-8B](https://huggingface.co/IIGroup/X-Coder-SFT-Qwen3-8B)
- **Training Method**: GRPO
- **Training Data**: [IIGroup/X-Coder-RL-40k](https://huggingface.co/datasets/IIGroup/X-Coder-RL-40k)
- **Parameters**: 8B

## Training

This model was trained using the X-Coder RLVR recipe. For training details and code, please refer to the [X-Coder GitHub repository](https://github.com/JieWu02/X-Coder).

## Performance

**Average Performance on LiveCodeBench v5 & v6.**

![Results](results.png)

## Recommended Inference Parameters

| Parameter | Value |
|-----------|-------|
| temperature | 0.6 |
| top_p | 0.95 |
| top_k | 20 (or -1 to disable) |
| max_new_tokens | 32768 |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "IIGroup/X-Coder-RL-Qwen3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Write a Python function to solve the two sum problem."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=32768,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Citation

```bibtex
@misc{wu2026xcoderadvancingcompetitiveprogramming,
      title={X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests}, 
      author={Jie Wu and Haoling Li and Xin Zhang and Jiani Guo and Jane Luo and Steven Liu and Yangyu Huang and Ruihang Chu and Scarlett Li and Yujiu Yang},
      year={2026},
      eprint={2601.06953},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.06953}, 
}
```

## License

This project is licensed under the Apache License 2.0.