Datasets:

luca0621
/

appgen-ued-v32r12-training-data

license: other
license_name: appgen-generated-data-notice
license_link: LICENSE.md
task_categories:
  - reinforcement-learning
tags:
  - gui-agent
  - mobile-agent
  - android
  - reinforcement-learning
  - ued
  - grpo
  - reproducibility
pretty_name: AppGen UED-GRPO v32r12 Training Data and State

AppGen UED-GRPO v32r12 training bundle

This dataset is the reproducibility bundle for uedgpo_q3_ngc_amex_pbrs_v32r12. It fills the release gap left by the previous static-data and model repositories: the run's online-generated environments and its curriculum teacher states.

The bundle is designed to be materialized together with the already published, revision-pinned static inputs. No public benchmark, AndroidWorld evaluation, or held-out evaluation screenshots are included.

artifacts/v32r12_online_envs.tar.gz: all 19 online-generated environments produced by the run, including ui_structure.json, HTML, rendered PNGs, and generation metadata.
artifacts/v32r12_static_envs.tar.gz: all 150 static UED environments used by the run, packed for fast one-file transfer.
teacher_states/global_step_*/teacher_state.json: curriculum/replay state at every saved checkpoint from step 10 through step 110.
run/: exact curriculum seed, launcher registry, policy system prompt, and Kubernetes run spec.
manifests/: per-file SHA-256 hashes, per-environment inventory, static-input hashes, dependency revisions, and the release manifest.
download_and_materialize.py: one-command materializer and verifier.

The v32r12 static pool contains 150 environments under preload_v5/{base,easy,uniform}. Twelve are pinned warm-up seeds and 138 remain in the runtime unseen pool after seed exclusion. The same byte-identical files remain in luca0621/appgen-training-data at the pinned provenance revision, but are also bundled here to avoid enumerating thousands of small files from that large repository during materialization.

Quick start

Install the downloader, fetch this small script, and materialize the complete training-data layout:

pip install -U "huggingface_hub>=0.28.0"
hf download luca0621/appgen-ued-v32r12-training-data \
  download_and_materialize.py --repo-type dataset --local-dir v32r12-release
python v32r12-release/download_and_materialize.py \
  --output-dir ./appgen-v32r12-data

The default command downloads and verifies:

this release's online environments and teacher states;
the bundled 150 static environments, verified against the pinned appgen-training-data revision;
the exact sft_qwen3_UNIFIED.json used by the UED environment loader; and
the launcher registry and run metadata.

To also download the 18-GB initializer model:

python v32r12-release/download_and_materialize.py \
  --output-dir ./appgen-v32r12-data --include-model

After completion, the output reproduces the original relative layout beneath /data/appgen, including:

appgen-v32r12-data/
├── preload_v5/{base,easy,uniform}/
├── sft_qwen3_UNIFIED.json
├── training_env_pool/_launcher_registry.yaml
├── verl_q3_ngc_amex_pbrs_v32r12_pool/
│   ├── envs/
│   ├── grpo_curriculum_seed_category10_v28.json
│   └── system_prompt.sft_exact.txt
└── verl_q3_ngc_amex_pbrs_v32r12_ckpts/
    └── global_step_*/teacher_state.json

Pinned dependencies

Role	Repository	Revision
Static UED environments	`luca0621/appgen-training-data`	`fed731b4dfa58118cb3014cf2656a42b6ae92f0d`
SFT goal manifest	`luca0621/appgen-sft-data`	`c195ae15abd3d6aaa07f971b8d732e7a28fa8dbf`
NGC SFT source	`luca0621/appgen-sft-ngc-v1`	`769ea99dbc4ff190048ae0db37eb6310dba595e0`
AMEX materializer input	`Yuxiang007/AMEX`	`17196b29c88dd48a7fb90ef9131bc5c7bf39f26e`
UED initializer model	`namhokaist/appgen-qwen3-vl-8b-sft-ngc-amex-avariant-E-ngc-lr2p5e7-1ep`	`6cdf0aa413850771f9a6f4c4da38f53d9d060f1c`

The initializer's audited materialization receipt reports ngc_primary_rows=2768 and amex_rows=0 for arm E. AMEX is therefore a pinned materializer/provenance input but contributes no selected gradient row to this specific initializer. The selected NGC configuration has 3,588 exposures and 3,168 unique semantic examples.

Online-environment usage

All 19 generated environments are released, including those that were only generated and never retained in a saved replay buffer. At step 100, three online environments appear in the teacher buffer and each has one visit, which is score-only under the run contract. At step 110, one online environment has two visits and was therefore replayed for a gradient update. Raw teacher states are included so downstream users can audit this distinction directly.

Integrity

download_and_materialize.py verifies the archive SHA-256, every extracted online-environment file, all 150 static ui_structure.json files, the exact SFT JSON, every teacher state, and the run sidecars. The top-level release manifest records the same digests and source revisions.

Models

The corresponding public checkpoints include:

luca0621/appgen-qwen3-uedgrpo-ngc-amex-pbrs-v32r12-step100 at revision 58be2eec39b1f31291347b9153e55d7ccec91b5d
luca0621/appgen-qwen3-uedgrpo-ngc-amex-pbrs-v32r12-step110 at revision 72f05813148c2f1174447490b15abb3a9abb0445

Model repositories contain weights and model provenance. This dataset contains the environment and curriculum state needed to reproduce and audit training.