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:
{
"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sft_qwen25_UNIFIED_V2.json— UNIFIED + easy band only (length-skewed; superseded by V3)sft_qwen25_UNIFIED.json,sft_qwen3_UNIFIED.json— long-task-only originalssft_data_mobile_*_ALIGNED.json— earlier aligned variants