README.md

metadata

license: apache-2.0
datasets:
  - ssssmark/AesCoT
metrics:
  - spearmanr
  - pearsonr
base_model:
  - Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: reinforcement-learning

Unlocking the Essence of Beauty: Advanced Aesthetic Reasoning with Relative-Absolute Policy Optimization

A novel and effective reinforcement learning framework designed for Image Aesthetic Assessment and general open-ended preference evaluation.

🖥️Training

Preparation

1. First download the IAA datasets(AVA,TAD66K,AADB,PARA...) and place them all in a single folder.
2. Construct your image-score dataset in the following format:

{
    "messages": [
      {
        "content": "prompt here",
        "role": "user"
      },
      {
        "content": "response here",
        "role": "assistant"
      }
    ],
    "images": "image_path_1"
  },

we provide an example dataset in AesR1/data folder. 3. Download the pre-trained model weights from here and place them in AesR1/models

Cold-start

We use LLaMA-Factory to train the SFT model.

1. Clone the LLaMA-Factory repository and install the dependencies.

git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n coldstart python=3.11.10
conda activate coldstart
cd LLaMA-Factory
pip install -e ".[torch,metrics]"

2. Put your cot dataset info in LLaMA-Factory/data/dataset_info.json and move qwen_aescot.yaml into LLaMA-Factory/examples/train_full
3. Run the following command to train the SFT model.

llamafactory-cli train examples/train_full/qwen_aescot.yaml

RAPO

First setup the environment for RAPO training.

conda create -n rapo python=3.11.10
conda activate rapo
bash setup.sh

After modification, run the following command to train the RAPO model.

# For single node training
bash train/rapo/src/open-r1-multimodal/run_scripts/Aes/aes_onenode.sh

# For multi node training
bash train/rapo/src/open-r1-multimodal/run_scripts/Aes/aes_multinode.sh

Inference

After training, you can inference the model by using the scripts in LLaMA-Factory.

#Install vllm
pip install vllm

#Infer
python scripts/vllm_infer.py \
--model_name_or_path [path/to/your/model] \
--dataset [dataset_name] \
--template qwen2_vl  \
--save_name result.jsonl \
--temperature 0.6 \

📚 Citation

If you find this repo useful, please consider citing our paper as follows:

@misc{liu2025unlockingessencebeautyadvanced,
      title={Unlocking the Essence of Beauty: Advanced Aesthetic Reasoning with Relative-Absolute Policy Optimization}, 
      author={Boyang Liu and Yifan Hu and Senjie Jin and Shihan Dou and Gonglei Shi and Jie Shao and Tao Gui and Xuanjing Huang},
      year={2025},
      eprint={2509.21871},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.21871}, 
}