SCOPE-RL-9B

SCOPE-RL-9B

SCOPE-RL-9B is a computer-use agent (CUA) trained under the SCOPE (Safety and Capability Optimization for Policy Execution) framework to balance task-execution capability with safety-aware decision-making. The model is initialized from SCOPE-SFT-9B and further optimized through online reinforcement learning on verifiable capability tasks.

In our evaluation, SCOPE-RL-9B achieves a 54.17% task success rate on OSWorld and a 64.30% attack-avoidance rate on OS-BLIND, corresponding to a capability-safety harmonic mean of 58.80%. Under our evaluation setting and among the models listed below, SCOPE-RL-9B achieves the best overall balance between capability and safety.

SCOPE-RL-9B is trained with programmatically verified capability tasks generated by SCOPE-Gen, using Safactory as the online reinforcement learning framework. Its initialization checkpoint, SCOPE-SFT-9B, was previously trained on capability demonstrations, safe-continuation trajectories, and explicit-refusal trajectories.

Links

• Paper: Beyond Task Completion: Training Capable and Safe Computer-Use Agents
• Data generation code: SCOPE-Gen
• RL training code: Safactory
• Model collection: SCOPE

Quick Start

Install vLLM:

pip install -U vllm

Launch an OpenAI-compatible inference server:

vllm serve k4ng/SCOPE-RL-9B \
    --host 0.0.0.0 \
    --port 8000 \
    --tensor-parallel-size 1 \
    --data-parallel-size 2 \
    --trust-remote-code \
    --served-model-name scope-rl

Results

All values are percentages, and ↑ indicates that higher is better. (H) is the harmonic mean of the OSWorld task success rate and the OS-BLIND attack-avoidance rate. Compared with SCOPE-Capability-Safety, SCOPE-RL-9B improves OSWorld performance by 4.45 percentage points, while attack avoidance decreases by 2.00 points. The harmonic mean increases from 56.83% to 58.80%.

License

This model is subject to the license terms of its base model, Qwen3.5-9B. Licensing information for the code is available in the corresponding GitHub repositories.

Citation

If you use SCOPE-RL, SCOPE-SFT, SATraj-OS, or SCOPE-Gen, please cite:

@misc{kang2026scope,
  title  = {Beyond Task Completion: Training Capable and Safe Computer-Use Agents},
  author = {Zeyu Kang and Zhenyun Yin and Yang Zhang and Shan He and Shanzhe Lei and Yanjiu Zhong and Xinquan Chen and Xuhong Wang},
  year   = {2026}
}