---
license: cc-by-nc-4.0
language:
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- chart-to-code
- multimodal
- vision-language
- reinforcement-learning
- self-correction
- matplotlib
---

# MM-ReCoder

<p align="center">
  <a href="https://cvpr.thecvf.com/Conferences/2026"><b>CVPR 2026</b></a>
  &nbsp;|&nbsp;
  <a href="https://zitiantang.github.io/MM-ReCoder/">Project Page</a>
  &nbsp;|&nbsp;
  <a href="https://arxiv.org/abs/2604.01600">arXiv</a>
  &nbsp;|&nbsp;
  <a href="https://github.com/ZitianTang/MM-ReCoder">Code</a>
  &nbsp;|&nbsp;
  <a href="https://huggingface.co/cwbc/MM-ReCoder-SFT-Cold-Start">SFT Cold-Start</a>
</p>

**MM-ReCoder** is the 7B vision-language model from the CVPR 2026 paper
[*MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction*](https://arxiv.org/abs/2604.01600).
It converts a chart image into the matplotlib code that reproduces it. At
inference time the model renders its own code with a sandboxed matplotlib
tool, inspects the result, and self-corrects across multiple turns.

This is the **final** RL-trained checkpoint. It is fine-tuned from
[`Qwen/Qwen2.5-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
via:

1. **SFT cold-start** — released separately as
   [`cwbc/MM-ReCoder-SFT-Cold-Start`](https://huggingface.co/cwbc/MM-ReCoder-SFT-Cold-Start).
2. **Multi-turn RL (GRPO), stage 1** — shared-first-turn optimization.
3. **Multi-turn RL (GRPO), stage 2** — full-trajectory optimization,
   resumed from stage 1.

## Usage

The recommended way to use MM-ReCoder is through the inference scripts in
the [official repository](https://github.com/ZitianTang/MM-ReCoder), which
wrap the model with the self-correction agent loop (render → critique →
revise):

```bash
git clone https://github.com/ZitianTang/MM-ReCoder.git
cd MM-ReCoder
# Follow the Installation section in the repo README.

# Downalod the MM-ReCoder checkpoint from Hugging Face
hf download cwbc/MM-ReCoder

# Two-turn self-correction on ChartMimic.
bash examples/mmrecoder/inference/chartmimic_2turns.sh
```

### Direct single-turn use (no self-correction)

You can also load the model in a single-pass setting via `transformers`:

```python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from PIL import Image
import torch

model_id = "cwbc/MM-ReCoder"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

image = Image.open("path/to/chart.png").convert("RGB")
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Generate the matplotlib code that reproduces this chart."},
    ],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

out = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(processor.batch_decode(out[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0])
```

This emits code in one shot. The full self-correction behavior requires the
agent loop in the repository.

## Training

- **Base model:** Qwen2.5-VL-7B-Instruct.
- **RL algorithm:** GRPO with chart-specific rule-based rewards (format,
  color, text, layout, type) plus an LLM-as-a-judge model reward.
- **RL data:** [Chart2Code-160k](https://huggingface.co/datasets/xxxllz/Chart2Code-160k)
  prompts.
- **Evaluation:**
  [ChartMimic](https://github.com/ChartMimic/ChartMimic) (direct-600),
  [Plot2Code](https://github.com/TencentARC/Plot2Code), and
  [ChartX](https://github.com/InternScience/ChartVLM).

See the [repository](https://github.com/ZitianTang/MM-ReCoder) for full
training scripts and configs.

## Citation

```bibtex
@inproceedings{tang2026mmrecoder,
    title={MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction},
    author={Zitian Tang and Xu Zhang and Jianbo Yuan and Yang Zou and Varad Gunjal and Songyao Jiang and Davide Modolo},
    booktitle={CVPR},
    year={2026}
}
```

## License

Released under the Apache 2.0 License, inheriting from the base
Qwen2.5-VL-7B-Instruct license.