---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-3B-Instruct
tags:
  - multimodal
  - vision-language
  - visual-reasoning
  - reinforcement-learning
  - qwen2.5-vl
  - math
  - reasoning
datasets:
  - OpenMMReasoner-Data
language:
  - en
pipeline_tag: image-text-to-text
library_name: transformers
---

# Frankenstein-IN

**Frankenstein-IN** is the supervised fine-tuned (cold-start initialization) model from the paper:

> **[What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis](https://arxiv.org/abs/2602.12395)**
>
> Xirui Li\*, Ming Li\*, Tianyi Zhou
>
> University of Maryland  |  Mohamed bin Zayed University of Artificial Intelligence
>
> *(\* Co-first Authors)*

This model serves as the **IN (Instruction-tuned) checkpoint** before reinforcement learning, built on the [OpenMMReasoner](https://arxiv.org/abs/2511.16334) training recipe with [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) as the base model.

## Overview

Our paper introduces a **Frankenstein-style analysis framework** to understand *what* reinforcement learning (RL) actually improves in vision-language models (VLMs) for visual reasoning. Rather than relying on end-to-end benchmark scores, we decompose VLMs at the granularity of transformer layers and probe their functional roles through:

1. **Functional Localization via Causal Probing** — localizing vision- and reasoning-related computations along transformer depth
2. **Update Characterization via Parameter Comparison** — showing that IN and RL differ systematically in update magnitude and geometry
3. **Transferability Test via Model Merging** — transplanting RL-refined regions into IN models to test causal contributions

### Key Findings

- RL does **not** uniformly improve visual perception or standalone reasoning
- RL induces **structured refinements concentrated in mid-to-late layers**, improving vision-to-reasoning alignment
- These mid-to-late refinements are both **transferable** (via merging) and **necessary** (via freezing) for RL gains
- Freezing **late layers** during RL training leads to a pronounced drop in reasoning performance

## Evaluation Results

### Fine-grained and Benchmark Metrics

| Model | Vision (M_vis) | Vision-to-Reasoning (M_v2r) | Reasoning (M_rea) | MathVista | MathVision | MathVerse |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|
| **Frankenstein-IN** (this model) | 34.0 | 21.0 | 26.0 | 46.5 | 18.4 | 37.0 |
| Frankenstein-RL | 33.0 | 29.0 | 34.0 | 48.1 | 14.1 | 37.8 |

### Parameter Freezing Analysis (RL Training)

| Model | Vision (M_vis) | Vision-to-Reasoning (M_v2r) | Reasoning (M_rea) | MathVista | MathVision | MathVerse |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|
| RL - Frozen **Early** Block | **35.0** | **31.0** | 36.0 | **48.2** | **21.0** | 34.5 |
| RL - Frozen **Mid** Block | 25.0 | 29.0 | **38.0** | 46.5 | 15.5 | **35.7** |
| RL - Frozen **Late** Block | 30.0 | 27.0 | 34.0 | 47.9 | 16.8 | 35.0 |

## Quick Start

### Installation

```bash
pip install transformers accelerate
pip install qwen-vl-utils[decord]==0.0.8
```

### Inference

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "AIcell/Frankenstein-IN",
    torch_dtype="auto",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("AIcell/Frankenstein-IN")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://your-image-url.jpg"},
            {"type": "text", "text": "Please solve this math problem step by step."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```

## Related Resources

| Resource | Link |
|:---|:---|
| Paper | [arXiv:2602.12395](https://arxiv.org/abs/2602.12395) |
| Frankenstein-RL Model | [AIcell/Frankenstein-RL](https://huggingface.co/AIcell/Frankenstein-RL) |
| Base Model | [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) |
| OpenMMReasoner | [arXiv:2511.16334](https://arxiv.org/abs/2511.16334) |

## Citation

```bibtex
@article{li2026frankenstein,
  title={What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis},
  author={Li, Xirui and Li, Ming and Zhou, Tianyi},
  journal={arXiv preprint arXiv:2602.12395},
  year={2026}
}
```

## License

This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).