UnifiedReward-qwen-7B

We are actively gathering feedback from the community to improve our models. We welcome your input and encourage you to stay updated through our repository!!

Model Summary

UnifiedReward-qwen-7b is the first unified reward model based on Qwen/Qwen2.5-VL-7B-Instruct for multimodal understanding and generation assessment, enabling both pairwise ranking and pointwise scoring, which can be employed for vision model preference alignment.

For further details, please refer to the following resources:

• 📰 Paper: https://arxiv.org/pdf/2503.05236
• 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
• 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
• 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
• 👋 Point of Contact: Yibin Wang

🏁 Compared with Current Reward Models

Reward Model	Method	Image Generation	Image Understanding	Video Generation	Video Understanding
PickScore	Point	√
HPS	Point	√
ImageReward	Point	√
LLaVA-Critic	Pair/Point		√
IXC-2.5-Reward	Pair/Point		√		√
VideoScore	Point			√
LiFT	Point			√
VisionReward	Point	√		√
VideoReward	Point			√
UnifiedReward (Ours)	Pair/Point	√	√	√	√

Quick Start

All pair rank and point score inference codes are provided in our github.

We take image understanding assessment as example here:

import json
import random
import torch
import tqdm
from PIL import Image
import warnings
import os
from transformers import AutoProcessor, AutoTokenizer, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info

warnings.filterwarnings("ignore")

model_path = "CodeGoat24/UnifiedReward-qwen-7b"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)


url = "https://github.com/LLaVA-VL/blog/blob/main/2024-10-03-llava-critic/static/images/critic_img_seven.png?raw=True"
image = Image.open(requests.get(url, stream=True).raw)

prompt_text = f'Given an image and a corresponding question, please serve as an unbiased and fair judge to evaluate the quality of the answers provided by a Large Multimodal Model (LMM). Determine which answer is better and explain your reasoning with specific details. Your task is provided as follows:\nQuestion: [What this image presents?]\nThe first response: [The image is a black and white sketch of a line that appears to be in the shape of a cross. The line is a simple and straightforward representation of the cross shape, with two straight lines intersecting at a point.]\nThe second response: [This is a handwritten number seven.]\nASSISTANT:\n'

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": prompt_text},
        ],
    }
]

chat_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[chat_input],
    images=image_inputs,
    videos=video_inputs,
    return_tensors="pt",
    padding=True
).to("cuda")

with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=4096)
generated_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output = processor.batch_decode(generated_trimmed, skip_special_tokens=True)[0]


print(output)

Citation

@article{UnifiedReward,
  title={Unified Reward Model for Multimodal Understanding and Generation.},
  author={Wang, Yibin and Zang, Yuhang, and Li, Hao and Jin, Cheng and Wang Jiaqi},
  journal={arXiv preprint arXiv:2503.05236},
  year={2025}
}