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README.md

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+---
+license: apache-2.0
+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
+- sft
+- cold-start
+- matplotlib
+---
+
+# MM-ReCoder-SFT-Cold-Start
+
+<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">Final RL Model</a>
+</p>
+
+**MM-ReCoder-SFT-Cold-Start** is the supervised fine-tuned cold-start
+checkpoint released alongside the CVPR 2026 paper
+[*MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction*](https://arxiv.org/abs/2604.01600).
+It is fine-tuned from
+[`Qwen/Qwen2.5-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
+to bootstrap the chart-to-code and self-correction behaviors before the
+multi-turn RL stages.
+
+> **This is an intermediate checkpoint**, not the final MM-ReCoder model.
+> If you want the best chart-to-code performance, use
+> [`cwbc/MM-ReCoder`](https://huggingface.co/cwbc/MM-ReCoder) instead.
+> This checkpoint is released for researchers who want to reproduce or
+> ablate the RL stages of the paper.
+
+## Intended Use
+
+This checkpoint is intended as the **starting point for multi-turn RL**
+training. The pipeline is:
+
+1. **SFT cold-start** *(this checkpoint)* — Qwen2.5-VL-7B-Instruct fine-tuned
+   on chart-to-code demonstrations.
+2. **Multi-turn RL (GRPO), stage 1** — shared-first-turn optimization,
+   initialized from this checkpoint.
+3. **Multi-turn RL (GRPO), stage 2** — full-trajectory optimization, resumed
+   from stage 1. The result is released as
+   [`cwbc/MM-ReCoder`](https://huggingface.co/cwbc/MM-ReCoder).
+
+## Usage
+
+To kick off RL from this cold-start checkpoint, clone the
+[official repository](https://github.com/ZitianTang/MM-ReCoder) and run the
+stage 1 training script (which references this checkpoint via
+`REF_MODEL_PATH=cwbc/MM-ReCoder-SFT-Cold-Start`):
+
+```bash
+git clone https://github.com/ZitianTang/MM-ReCoder.git
+cd MM-ReCoder
+# Follow the Installation section in the repo README, then launch the
+# LLM-as-a-judge reward server (see the RL Training section).
+
+# Stage 1: multi-turn GRPO with a shared first turn.
+bash examples/mmrecoder/train/stage1-shared-first-turn.sh
+
+# Stage 2: multi-turn GRPO on the full trajectory, resumed from stage 1.
+bash examples/mmrecoder/train/stage2-full-trajectory.sh
+```
+
+### Multi-Turn Inference with the Cold-Start Model
+
+This checkpoint also supports the multi-turn self-correction inference
+loop from the repository — useful for measuring the RL gains over the
+SFT-only baseline. Reuse the inference scripts and override the model path:
+
+```bash
+# Download the cold-start checkpoint.
+hf download cwbc/MM-ReCoder-SFT-Cold-Start
+
+# Two-turn self-correction on ChartMimic, using the cold-start model.
+bash examples/mmrecoder/inference/chartmimic_2turns.sh \
+    model.path=cwbc/MM-ReCoder-SFT-Cold-Start \
+    data.output_path=generations/coldstart_chartmimic_2turns.json
+```
+
+The self-correction *policy* is sharpened by the RL stages, so the
+cold-start model will generally underperform [`cwbc/MM-ReCoder`](https://huggingface.co/cwbc/MM-ReCoder)
+on multi-turn benchmarks; this is the intended baseline comparison.
+
+### Direct single-turn use
+
+You can also load the checkpoint directly with `transformers` to inspect
+single-turn chart-to-code behavior:
+
+```python
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+import torch
+
+model_id = "cwbc/MM-ReCoder-SFT-Cold-Start"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+```
+
+## 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.