CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
- Published
- Source
- arXiv
- Paper number
- 242
- Field
- AI / General
- arXiv ID
- 2605.25624
Key points
- The discriminator is isolated from the generator's implementation details and sees only the task t and the resulting environment state, and it then writes a reward.py script that should return 0.0 for the initial state and 1.0 for the golden state.
- The Qwen3.5-35B model (A3B) improved success rate from 54.5 percent to 62.1 percent.
- The Qwen3.5-397B model (A17B) reached a 72.6 percent success rate, which is an increase of more than 10 percentage points.
Paper links
External research summaries. These are not HDATF publications or measured product results.