# appgen-sft-data — SFT datasets for mobile-GUI agents

Step-level SFT samples harvested from the GRPO simulator environments
(`appgen-training-data`). One sample = one action step:

```json
{
  "messages": [
    {"role": "user", "content": "<image> Goal: <task with app name> History: step1: ...; step2: ..."},
    {"role": "assistant", "content": "<think>one-sentence reasoning</think>\n<tool_call>\n{\"name\": \"mobile_use\", \"arguments\": {...}}\n</tool_call>"}
  ],
  "images": ["<abs path to page screenshot>"],
  "action_type": "TAP|SWIPE|open_app|TYPE|PRESS_ENTER|TASK_COMPLETE|...",
  "env_dir": "...", "task_id": "...", "step": 0, "path_length": 7
}
```

- qwen25 variants: **absolute** coordinates in the smart_resize'd image space
  (`click [x,y]`); qwen3 variants: **0-1000 normalized**.
- Entry mix ≈ 54% `open_app` / 46% drawer-swipe across all versions.
- Every trajectory ends with `terminate(status="success")` at the target page.

## Current (use these)

| file | model | trajectories | notes |
|---|---|---|---|
| `sft_qwen25_UNIFIED_V3.json` | Qwen2.5-VL (abs) | 554 traj / 6,853 samples / 100 envs | **length-uniform**: path 3–20 ≈30 each; thinks 100% |
| `sft_qwen3_UNIFIED_V3.json`  | Qwen3-VL (norm)  | same content, normalized coords | |

Images: extract `unified_sft_assets.tar.gz` (preload_v3 envs) and
`v4sel_sft_assets.tar.gz` (50 fresh-pool envs), then rewrite the two path
prefixes `/data/appgen/preload_v3` and `/data/appgen/training_env_pool_v4`
to your extraction root. System prompts: `unified_prompts.tar.gz`.

## Older versions (provenance / ablations)
- `sft_qwen25_UNIFIED_V2.json` — UNIFIED + easy band only (length-skewed; superseded by V3)
- `sft_qwen25_UNIFIED.json`, `sft_qwen3_UNIFIED.json` — long-task-only originals
- `sft_data_mobile_*_ALIGNED.json` — earlier aligned variants