CODA

This repository is the official implementation of CODA.

CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
Zeyi Sun*, Yuhang Cao*, Jianze Liang*, Qiushi Sun*, Ziyu Liu*, Zhixiong Zhang, Yuhang Zang, Xiaoyi Dong, Kai Chen, Dahua Lin, Jiaqi Wang

📖Paper | 🤗CODA-PLANNER-TARS-32B

👨‍💻 Todo

• Training code of CODA based on OpenRLHF.
• Release of JudgeModel finetuned on Qwen2.5-VL-72B.
• Inference code of CODA on ScienceBoard.
• Release of CODA-PLANER-TARS-32B.

🛠️ Usage

Installation

conda create -n coda python=3.11 
conda activate coda
pip install vllm==0.8.5.post1

Inference

Prepare ScienceBoard environment replace sci folder in ScienceBoard with our ScienceBoard_CODA/sci and put qwenvl_test.py under ScienceBoard base folder.

# use conda (vllm==0.8.5.post1) to deploy model to reproduce our results.
# deploy CODA-PLANER-TARS-32B
vllm serve OpenIXCLab/CODA-PLANNER-TARS-32B \
    --served-model-name "qwen32b" \
    --host 0.0.0.0 \
    --port "${PORT_1}" \
    --tensor-parallel-size 4 &

# deploy executor UI-TARS-1.5-7B
CUDA_VISIBLE_DEVICES=4,5 vllm serve ByteDance-Seed/UI-TARS-1.5-7B \
    --served-model-name "tars1.5-grounding" \
    --host 0.0.0.0 \
    --port "${PORT_2}" \
    --tensor-parallel-size 2 &

# in sciboard env, perform agent evaluation.
export SOFTWARE='Celestia'
export SUBFOLDER="planner_ans"
export DEBUG_LOG=0
export SERVER_URL="http://YOUR.PLANER.ADDR:PORT_1/v1/chat/completions" # qwen32b for baseline and coda-1.0-32b for our planner
export EXECUTOR_URL="http://YOUR.EXECUTOR.ADDR:PORT_2" # uitars-1.5 addr
export MODEL_NAME="qwen32b"
export NO_CONTEXT_IMAGE=0
export SPLITE=8
export QWEN_PLANNER=1
export PLANNER_ANS=1

for i in {0..7}; do # parallel for 8 VMs
    export VM_PATH="vmware_vm_data/Ubuntu${i}/Ubuntu${i}.vmx" 
    # Set port based on i value
    export INDEX=$i
    if [ $i -eq 0 ]; then
        # Process i=0: show output in terminal
        timeout 90m python qwenvl_test.py &
    else
        # Process i>0: redirect output to log file
        timeout 90m python qwenvl_test.py > "logs/vm${i}_output.log" 2>&1 &
    fi

    sleep 10s
done
wait
sleep 10s
echo "All tasks completed."

✒️ Citation

@misc{sun2025codacoordinatingcerebrumcerebellum,
      title={CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning}, 
      author={Zeyi Sun and Yuhang Cao and Jianze Liang and Qiushi Sun and Ziyu Liu and Zhixiong Zhang and Yuhang Zang and Xiaoyi Dong and Kai Chen and Dahua Lin and Jiaqi Wang},
      year={2025},
      eprint={2508.20096},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.20096}, 
}


@misc{sun2025seagentselfevolvingcomputeruse,
      title={SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience}, 
      author={Zeyi Sun and Ziyu Liu and Yuhang Zang and Yuhang Cao and Xiaoyi Dong and Tong Wu and Dahua Lin and Jiaqi Wang},
      year={2025},
      eprint={2508.04700},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2508.04700}, 
}

📄 License

Usage and License Notices: The data and code are intended and licensed for research use only. License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use

Acknowledgement

We sincerely thank projects UI-TARS, ScienceBoard, R1-V, for providing their open-source resources.