# Mobile R1 Benchmark Eval

> Evaluates a vision-language model's ability to navigate and complete multi-step tasks on mobile GUIs. It measures step-level action correctness, full trajectory success, and robustness to intermediate errors in a simulated Android environment. Use when the user wants to benchmark on Chinese Mobile Agent Benchmark, or asks about evaluating this task. Reports Accuracy (Acc.).

- Skill: `qhjqhj00/mobile-r1-benchmark-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/mobile-r1-benchmark-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/mobile-r1-benchmark-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/mobile-r1-benchmark-eval

---


# mobile-r1-benchmark-eval

> Mobile-R1: Towards Interactive Reinforcement Learning for VLM-Based Mobile Agent via Task-Level Rewards — Gu et al. (2025) (arXiv:2506.20332, 2025)

## What this evaluates

Evaluates a vision-language model's ability to navigate and complete multi-step tasks on mobile GUIs. It measures step-level action correctness, full trajectory success, and robustness to intermediate errors in a simulated Android environment.

## Datasets

- **Chinese Mobile Agent Benchmark** — total 500; splits: test (500)

## Metrics

- `Accuracy (Acc.)` **(primary)** — range: percent
  - Percentage of steps in a trajectory where both the predicted action format and action type match the ground truth definitions.
- `Task Success Ratio (Task Succ.)` — range: percent
  - Percentage of trajectories where every single step is executed entirely correctly without any deviations.
- `Tail Success Ratio (Tail Succ)` — range: percent
  - Percentage of trajectories that ultimately complete the final task goal, regardless of intermediate errors or deviations.
- `Action Argument Error Number (Avg Err.)` — range: other
  - Average count of incorrect action arguments per trajectory.

## Input / output format

**Input**: Task instruction and sequential mobile GUI screenshots (visual state) provided to a VLM.

**Output**: A sequence of structured actions per step, specifying action type and required parameters (e.g., click coordinates, text input).

## Scoring recipe

```python
def score_trajectory(pred_steps, gold_steps):
    step_acc = sum(1 for p, g in zip(pred_steps, gold_steps) if p.format == g.format and p.action == g.action) / len(gold_steps)
    task_succ = 1.0 if all(p.format == g.format and p.action == g.action for p, g in zip(pred_steps, gold_steps)) else 0.0
    tail_succ = 1.0 if pred_steps[-1].final_state == gold_steps[-1].goal_state else 0.0
    err_count = sum(1 for p, g in zip(pred_steps, gold_steps) if p.action != g.action)
    return step_acc * 100, task_succ * 100, tail_succ * 100, err_count
```

## Common pitfalls

- Confusing step-level Accuracy with Task Success Ratio; the former allows intermediate failures while the latter requires 100% step correctness.
- Tail Success Ratio explicitly ignores intermediate deviations, so evaluators must track the final task completion state rather than penalizing every wrong click.
- Avg Err. is a raw count of incorrect actions, not a normalized ratio, so it should not be averaged across trajectories without weighting by trajectory length.

## Evidence (verbatim from paper)

> We evaluate the model’s performance using the following metrics: Accuracy (Acc.): The probability of correctly performing each step in a trajectory, correct if both format and action match definitions R_F and R_Act. • Task Success Ratio (Task Succ.): The probability of a complete trajectory being executed entirely correctly. • Tail Success Ratio (Tail Succ): The probability that the task within a trajectory is ultimately completed successfully, regardless of intermediate errors or deviations. • Action Argument Error Number (Avg Err.): The count of errors of incorrect action.

## Citation

```bibtex
@misc{gu2025mobiler1,
  title={Mobile-R1: Towards Interactive Reinforcement Learning for VLM-Based Mobile Agent via Task-Level Rewards},
  author={Gu et al. (2025)},
  year={2025},
  note={arXiv:2506.20332}
}
```

- arXiv: 2506.20332