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
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 training recipe with 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:
- Functional Localization via Causal Probing — localizing vision- and reasoning-related computations along transformer depth
- Update Characterization via Parameter Comparison — showing that IN and RL differ systematically in update magnitude and geometry
- 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
pip install transformers accelerate
pip install qwen-vl-utils[decord]==0.0.8
Inference
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 |
| Frankenstein-RL Model | AIcell/Frankenstein-RL |
| Base Model | Qwen/Qwen2.5-VL-3B-Instruct |
| OpenMMReasoner | arXiv:2511.16334 |
Citation
@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.