training_sets/README.md

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:

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.