Closing the Specialization Gap between Computer Use Benchmark Scores and the Real World Performance

Strong general computer-use performance does not guarantee reliability in a narrow production workflow. We study this specialization gap in browser-based booking, where an agent operates from screenshots, asks for missing information, and must report the outcome truthfully. Although Qwen3.5-35B-A3B reports 54.5% on OSWorld-Verified, it performs poorly under our all-or-nothing booking criterion. We adapt it in two stages: supervised fine-tuning (SFT) teaches the production protocol, tool use, and user interaction, and reinforcement learning (RL) aligns complete trajectories with product logic. Training uses a browser harness that reproduces the production model-facing protocol and an Iron User simulator that answers clarification questions while updating the task ground truth. Evaluation combines local LLM judgments over screenshots, actions, and dialogue with deterministic resolution. On the available evaluation subset, SFT raises task success from 16.7% to 74.1%, and the SFT-plus-RL model outperforms all unadapted systems in the reported RL comparison.