| --- |
| 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. |
|
|
| ## Contents |
|
|
| - `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: |
|
|
| ```bash |
| 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: |
|
|
| 1. this release's online environments and teacher states; |
| 2. the bundled 150 static environments, verified against the pinned |
| `appgen-training-data` revision; |
| 3. the exact `sft_qwen3_UNIFIED.json` used by the UED environment loader; and |
| 4. the launcher registry and run metadata. |
|
|
| To also download the 18-GB initializer model: |
|
|
| ```bash |
| 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: |
|
|
| ```text |
| 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. |
|
|
