---
pipeline_tag: image-text-to-text
library_name: transformers
license: mit
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- Multimodal Reward Model
- Reward Model
---

<div align="center">   
  <img src="skywork-logo.png" alt="Introduction Image" width="500" height="400"> 
</div>

## 🔥News

**May 12, 2025**: Our technical report is now available on arXiv and we welcome citations：[Skywork-VL Reward: An Effective  Reward Model for Multimodal Understanding and Reasoning](https://arxiv.org/abs/2505.07263)

**April 24, 2025**: We released **Skywork-VL-Reward-7B**, A state-of-the-art multimodal reward model on [VLRewardBench](https://huggingface.co/spaces/MMInstruction/VL-RewardBench), and have released our technical report on the [R1V GitHub](https://github.com/SkyworkAI/Skywork-R1V/blob/main/SkyworkVL_RM.pdf) repository.

## Introduction
The lack of multimodal reward models on the market has become a major bottleneck restricting the development of multimodal reinforcement technology.
We open source the 7B multimodal reward model Skywork-VL-Reward, injecting new momentum into the industry and opening a new chapter in multimodal reinforcement learning


Skywork-VL-Reward is based on the [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) architecture with the addition of a value head structure for training reward model.
We obtained SOTA of 73.1 in [VL-RewardBench](https://vl-rewardbench.github.io/) and high score of 90.1 in [RewardBench](https://huggingface.co/spaces/allenai/reward-bench).
In addition, our MPO trained on Skywork-R1V-2.0 further validates the effectiveness of the model.
We hope that this multimodal reward model will contribute to the open source community!
Please refer to our technical report for more details.

## Technical Report
[Skywork-VL Reward: An Effective  Reward Model for Multimodal Understanding and Reasoning](https://arxiv.org/abs/2505.07263)

## Evaluation
<h3 align="center">VL-RewardBench</h3>
<table style="margin: auto;">
  <thead>
    <tr>
      <th>Model Name</th><th>Model Size</th><th>General</th><th>Hallucination</th><th>Reasoning</th><th>Overall Accuracy</th><th>Macro Average</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="7" align="center"><i>Proprietary Models</td></tr>
    <tr><td>Claude-3.5-Sonnet(2024-06-22)</td><td>-</td><td>43.4</td><td>55.0</td><td>62.3</td><td>55.3</td><td>53.6</td></tr>
    <tr><td>Gemini-1.5-Flash (2024-09-24)</td><td>-</td><td>47.8</td><td>59.6</td><td>58.4</td><td>57.6</td><td>55.3</td></tr>
    <tr><td>GPT-4o(2024-08-06)</td><td>-</td><td>49.1</td><td>67.6</td><td>70.5</td><td>65.8</td><td>62.4</td></tr>
    <tr><td>Gemini-1.5-Pro(2024-09-24)</td><td>-</td><td>50.8</td><td>72.5</td><td>64.2</td><td>67.2</td><td>62.5</td></tr>
    <tr><td>Gemini-2.0-flash-exp(2024-12)</td><td>-</td><td>50.8</td><td>72.6</td><td>70.1</td><td><strong>68.8</strong></td><td><strong>64.5</strong></td></tr>
    <tr><td colspan="7" align="center"><i>Open-Source Models</td></tr>
    <tr><td>Qwen2-VL-7B-Instruct</td><td>7B</td><td>31.6</td><td>19.1</td><td>51.1</td><td>28.3</td><td>33.9</td></tr>
    <tr><td>MAmmoTH-VL-8B</td><td>8B</td><td>36.0</td><td>40.0</td><td>52.0</td><td>42.2</td><td>42.7</td></tr>
    <tr><td>Qwen2.5-VL-7B-Instruct</td><td>7B</td><td>43.4</td><td>42.0</td><td>63.0</td><td>48.0</td><td>49.5</td></tr>
    <tr><td>InternVL3-8B</td><td>8B</td><td>60.6</td><td>44.0</td><td>62.3</td><td>57.0</td><td>55.6</td></tr>
    <tr><td>IXC-2.5-Reward-7B</td><td>7B</td><td>80.3</td><td>65.3</td><td>60.4</td><td>66.3</td><td>68.6</td></tr>
    <tr><td>Qwen2-VL-72B-Instruct</td><td>72B</td><td>38.1</td><td>32.8</td><td>58.0</td><td>39.5</td><td>43.0</td></tr>
    <tr><td>Molmo-72B-0924</td><td>72B</td><td>33.9</td><td>42.3</td><td>54.9</td><td>44.1</td><td>43.7</td></tr>
    <tr><td>QVQ-72B-Preview</td><td>72B</td><td>41.8</td><td>46.2</td><td>51.2</td><td>46.4</td><td>46.4</td></tr>
    <tr><td>Qwen2.5-VL-72B-Instruct</td><td>72B</td><td>47.8</td><td>46.8</td><td>63.5</td><td>51.6</td><td>52.7</td></tr>
    <tr><td>InternVL3-78B</td><td>78B</td><td>67.8</td><td>52.5</td><td>64.5</td><td>63.3</td><td>61.6</td></tr>
    <tr><td><strong>Skywork-VL Reward(Ours)</strong></td><td>7B</td><td>66.0</td><td>80.0</td><td>61.0</td><td><strong>73.1</strong></td><td><strong>69.0</strong></td></tr>
  </tbody>
</table>

---

<h3 align="center">RewardBench</h3>
<table style="margin: auto;">
  <thead>
    <tr>
      <th>Model Name</th><th>Chat</th><th>Chat Hard</th><th>Safety</th><th>Reasoning</th><th>Score</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="7" align="center"><i>Language-Only Reward Models</td></tr>
    <tr><td>InternLM2-7B-Reward</td><td>99.2</td><td>69.5</td><td>87.2</td><td>94.5</td><td>87.6</td></tr>
    <tr><td>Skywork-Reward-Llama3.1-8B</td><td>95.8</td><td>87.3</td><td>90.8</td><td>96.2</td><td>92.5</td></tr>
    <tr><td>Skywork-Reward-Llama-3.1-8B-v0.2</td><td>94.7</td><td>88.4</td><td>92.7</td><td>96.7</td><td>93.1</td></tr>
    <tr><td>QRM-Llama3.1-8B-v2</td><td>96.4</td><td>86.8</td><td>92.6</td><td>96.8</td><td><strong>93.1</strong></td></tr>
    <tr><td colspan="7" align="center"><i>Multi-Modal Reward Models</td></tr>
    <tr><td>Qwen2-VL-7B-Instruct</td><td>65.1</td><td>50.9</td><td>55.8</td><td>68.3</td><td>60.0</td></tr>
    <tr><td>InternVL3-8B</td><td>97.2</td><td>50.4</td><td>83.6</td><td>83.9</td><td>78.8</td></tr>
    <tr><td>Qwen2.5-VL-7B-Instruct</td><td>94.3</td><td>63.8</td><td>84.1</td><td>86.2</td><td>82.1</td></tr>
    <tr><td>IXC-2.5-Reward-7B</td><td>90.8</td><td>83.8</td><td>87.8</td><td>90.0</td><td>88.1</td></tr>
    <tr><td><strong>Skywork-VL Reward(Ours)</strong></td><td>90.0</td><td>87.5</td><td>91.1</td><td>91.8</td><td><strong>90.1</strong></td></tr>
  </tbody>
</table>

---


## Usage
### Set Up the Environment

```shell
conda create -n vl-reward python=3.11
conda activate vl-reward
bash setup.sh
```

### Run the Inference Code

```python
import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from trl import AutoModelForCausalLMWithValueHead
from qwen_vl_utils import process_vision_info
from transformers.utils import cached_file
from safetensors import safe_open


processor = AutoProcessor.from_pretrained("Skywork/Skywork-VL-Reward-7B")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Skywork/Skywork-VL-Reward-7B", min_pixels=min_pixels, max_pixels=max_pixels)

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Skywork/Skywork-VL-Reward-7B",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving
# pip install flash-attn --no-build-isolation
#
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "Skywork/Skywork-VL-Reward-7B",
#     device_map="auto",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
# )

model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
vhead_file = cached_file(
    path_or_repo_id="Skywork/Skywork-VL-Reward-7B", filename="value_head.safetensors"
)
with safe_open(vhead_file, framework="pt", device="cpu") as f:
    vhead_params = {key: f.get_tensor(key) for key in f.keys()}
model.load_state_dict(vhead_params, strict=False)
model.requires_grad_(False)
model.eval()

# score: 23.89
# if you use flash_attention_2 the score will be 23.76
demo_image = "demo.jpg"
demo_question = "Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\nQuestion: Is Purple the highest value?\nChoices:\n(A) no\n(B) yes"
demo_answer = "The answer is: B"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": demo_image,
            },
            {
                "type": "text",
                "text": demo_question,
            },
        ],
    },
    {
        "role": "assistant",
        "content": demo_answer,
    },
]
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=False
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")
values = model(**inputs, return_dict=True, use_cache=False)[-1]
scores = values.gather(
    dim=-1, index=(inputs["attention_mask"].sum(dim=-1, keepdim=True) - 1)
)
score = scores[0].item()
print("Reward Score is: ", score)
```

## Citation
If you use this work in your research, please cite:
```
@misc{wang2025skyworkvlrewardeffectivereward,
      title={Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning}, 
      author={Xiaokun Wang and Chris and Jiangbo Pei and Wei Shen and Yi Peng and Yunzhuo Hao and Weijie Qiu and Ai Jian and Tianyidan Xie and Xuchen Song and Yang Liu and Yahui Zhou},
      year={2025},
      eprint={2505.07263},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.07263}, 
}
```