| # Training sets of the offline-RL experiments |
|
|
| These are the six GRPO training sets behind the paper's training-data table. They are ready to train on. Each |
| set is 2,400 policy prompts, i.e. 16 prompts per step for 150 steps (one epoch) or 300 steps (two epochs). |
|
|
| | Set | UI-Genie | t | t-1 | Rows | |
| |---|---:|---:|---:|---:| |
| | `ui100` | 2,400 | – | – | 2,400 | |
| | `t100` | – | 2,400 | – | 2,400 | |
| | `ui75_t25` | 1,800 | 600 | – | 2,400 | |
| | `ui50_t25_t1_25` | 1,200 | 600 | 600 | 2,400 | |
| | `ui50_t35_t1_15` | 1,200 | 840 | 360 | 2,400 | |
| | `ui40_t35_t1_25` | 960 | 840 | 600 | 2,400 | |
|
|
| `t100` has 2,080 unique contexts plus 320 repeated ones. A repeated row carries a `__repN` suffix on its |
| `sample_id`. |
|
|
| ## Files |
|
|
| ``` |
| training_sets/ |
| <set>/ms_swift_grpo.jsonl one row per prompt, in training order |
| images/uigenie/ 8,534 screenshots (UI-Genie contexts) |
| images/af_t/ 7,262 screenshots (AndroidFlux t contexts) |
| images/af_t_minus_1/ 2,181 screenshots (AndroidFlux t-1 contexts) |
| sha256.json row count and SHA-256 of each ms_swift_grpo.jsonl |
| sources/ label-bearing t / t-1 samples the sets were built from (see Provenance) |
| ``` |
|
|
| The six sets share their screenshots: 17,977 files, 8.2 GB in total. Each file is named by its SHA-256. |
|
|
| Each row has these parts: |
|
|
| - `messages` and `images`: the policy prompt. This is the Qwen3-VL agent prompt with the latest 10 history steps |
| and the current screenshot. |
| - `reward_context`: the reward-model input. It holds the UI-Genie layout, the last ≤5 screens, the |
| screen resolution, the reference action and, for AndroidFlux rows, the `is_next_terminate` label. |
|
|
| Screenshot paths in both parts are relative to `training_sets/`, e.g. |
| `images/af_t/<sha256>.png`. |
|
|
| ## Training on a set |
|
|
| Rewrite the relative screenshot paths to absolute ones, then point the trainer at the file: |
|
|
| ```bash |
| cd training_sets |
| sed "s#\"images/#\"$PWD/images/#g" ui40_t35_t1_25/ms_swift_grpo.jsonl > ui40_t35_t1_25.abs.jsonl |
| ``` |
|
|
| The training code and configs are in the AndroidFlux repository under `rl/train/`: |
|
|
| - `rl/train/README.md`: one-command training and evaluation. |
| - `code/replay_recovery_benchmark/rl_training/configs/`: |
| - `b3_*` and `b4_*`: 150 steps. |
| - `b5_*_e2`: 300 steps. |
|
|
| Pass the file as `DATASET` (or `training.dataset`). |
|
|
| ## Provenance |
|
|
| The sets are built from these sources: |
|
|
| - `Gyubeum/UI_Genie_128k` @ `d7987e1f`. |
| - The `t` / `t_minus_1` slices of this repository: their `context.parquet` and `images.parquet`, and |
| `androidflux_tools/export_repaired_interchange.py`. |
| - `sources/t/samples.parquet` and `sources/t_minus_1/samples.parquet`: the `t` / `t_minus_1` samples with the |
| `is_next_terminate` and goal-match labels as they were when the sets were built. The top-level slices carry |
| later label corrections, which differ in 11 `is_next_terminate` values. |
|
|
| AndroidFlux contexts whose goal does not match their source trajectory are dropped. No rollout or reward-model |
| filtering is applied. |
|
|
| `rl/train/data_curation/reproduce.sh` rebuilds all six sets from those sources. These files are the ones the |
| runs trained on. Their content matches that rebuild hash for hash; only the screenshot root is rewritten to the |
| relative form above. |
|
|
| `curated_set/` in this repository lists the same rows as indices into the published slices. |
|
|
