Datasets:

luca0621
/

appgen-ued-v32r12-training-data

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
app_name: string
category: string
environment_path: string
generated_step: int64
page_count: int64
saved_teacher_states: struct<100: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_v (... 676 chars omitted)
  child 0, 100: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
      child 0, ducb_count: double
      child 1, last_seen: int64
      child 2, learnability: double
      child 3, level_id: string
      child 4, n_visits: int64
      child 5, num_episodes: int64
  child 1, 90: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
      child 0, ducb_count: double
      child 1, last_seen: int64
      child 2, learnability: double
      child 3, level_id: string
      child 4, n_visits: int64
      child 5, num_episodes: int64
  child 2, 110: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
      child 0, ducb_count: double
      child 1, last_seen: int64
      child 2, learnability: double
      child 3, level_id: string
      child 4, n_visits: int64
      child 5, num_episodes: int64
  child 3, 60: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
      child 0, ducb_count: double
      child 1, last_seen: int64
      child 2, learnability: d
...
d 3, level_id: string
      child 4, n_visits: int64
      child 5, num_episodes: int64
task_count: int64
ui_path: string
ui_sha256: string
sft_goal_manifest: struct<path: string, repo_id: string, repo_type: string, revision: string, sha256: string>
  child 0, path: string
  child 1, repo_id: string
  child 2, repo_type: string
  child 3, revision: string
  child 4, sha256: string
static_environments: struct<environment_count: int64, paths: list<item: string>, preload_digest: string, repo_id: string, (... 74 chars omitted)
  child 0, environment_count: int64
  child 1, paths: list<item: string>
      child 0, item: string
  child 2, preload_digest: string
  child 3, repo_id: string
  child 4, repo_type: string
  child 5, revision: string
  child 6, runtime_after_seed_exclusion: int64
initializer_sft: struct<amex_materializer_input: struct<repo_id: string, revision: string, selected_rows: int64>, ngc (... 104 chars omitted)
  child 0, amex_materializer_input: struct<repo_id: string, revision: string, selected_rows: int64>
      child 0, repo_id: string
      child 1, revision: string
      child 2, selected_rows: int64
  child 1, ngc: struct<repo_id: string, revision: string>
      child 0, repo_id: string
      child 1, revision: string
  child 2, selected_exposures: int64
  child 3, unique_semantic_examples: int64
initializer_model: struct<repo_id: string, repo_type: string, revision: string>
  child 0, repo_id: string
  child 1, repo_type: string
  child 2, revision: string
to
{'initializer_model': {'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string')}, 'initializer_sft': {'amex_materializer_input': {'repo_id': Value('string'), 'revision': Value('string'), 'selected_rows': Value('int64')}, 'ngc': {'repo_id': Value('string'), 'revision': Value('string')}, 'selected_exposures': Value('int64'), 'unique_semantic_examples': Value('int64')}, 'sft_goal_manifest': {'path': Value('string'), 'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string'), 'sha256': Value('string')}, 'static_environments': {'environment_count': Value('int64'), 'paths': List(Value('string')), 'preload_digest': Value('string'), 'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string'), 'runtime_after_seed_exclusion': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              app_name: string
              category: string
              environment_path: string
              generated_step: int64
              page_count: int64
              saved_teacher_states: struct<100: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_v (... 676 chars omitted)
                child 0, 100: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
                    child 0, ducb_count: double
                    child 1, last_seen: int64
                    child 2, learnability: double
                    child 3, level_id: string
                    child 4, n_visits: int64
                    child 5, num_episodes: int64
                child 1, 90: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
                    child 0, ducb_count: double
                    child 1, last_seen: int64
                    child 2, learnability: double
                    child 3, level_id: string
                    child 4, n_visits: int64
                    child 5, num_episodes: int64
                child 2, 110: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
                    child 0, ducb_count: double
                    child 1, last_seen: int64
                    child 2, learnability: double
                    child 3, level_id: string
                    child 4, n_visits: int64
                    child 5, num_episodes: int64
                child 3, 60: struct<ducb_count: double, last_seen: int64, learnability: double, level_id: string, n_visits: int64 (... 22 chars omitted)
                    child 0, ducb_count: double
                    child 1, last_seen: int64
                    child 2, learnability: d
              ...
              d 3, level_id: string
                    child 4, n_visits: int64
                    child 5, num_episodes: int64
              task_count: int64
              ui_path: string
              ui_sha256: string
              sft_goal_manifest: struct<path: string, repo_id: string, repo_type: string, revision: string, sha256: string>
                child 0, path: string
                child 1, repo_id: string
                child 2, repo_type: string
                child 3, revision: string
                child 4, sha256: string
              static_environments: struct<environment_count: int64, paths: list<item: string>, preload_digest: string, repo_id: string, (... 74 chars omitted)
                child 0, environment_count: int64
                child 1, paths: list<item: string>
                    child 0, item: string
                child 2, preload_digest: string
                child 3, repo_id: string
                child 4, repo_type: string
                child 5, revision: string
                child 6, runtime_after_seed_exclusion: int64
              initializer_sft: struct<amex_materializer_input: struct<repo_id: string, revision: string, selected_rows: int64>, ngc (... 104 chars omitted)
                child 0, amex_materializer_input: struct<repo_id: string, revision: string, selected_rows: int64>
                    child 0, repo_id: string
                    child 1, revision: string
                    child 2, selected_rows: int64
                child 1, ngc: struct<repo_id: string, revision: string>
                    child 0, repo_id: string
                    child 1, revision: string
                child 2, selected_exposures: int64
                child 3, unique_semantic_examples: int64
              initializer_model: struct<repo_id: string, repo_type: string, revision: string>
                child 0, repo_id: string
                child 1, repo_type: string
                child 2, revision: string
              to
              {'initializer_model': {'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string')}, 'initializer_sft': {'amex_materializer_input': {'repo_id': Value('string'), 'revision': Value('string'), 'selected_rows': Value('int64')}, 'ngc': {'repo_id': Value('string'), 'revision': Value('string')}, 'selected_exposures': Value('int64'), 'unique_semantic_examples': Value('int64')}, 'sft_goal_manifest': {'path': Value('string'), 'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string'), 'sha256': Value('string')}, 'static_environments': {'environment_count': Value('int64'), 'paths': List(Value('string')), 'preload_digest': Value('string'), 'repo_id': Value('string'), 'repo_type': Value('string'), 'revision': Value('string'), 'runtime_after_seed_exclusion': Value('int64')}}
              because column names don't match

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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.

Downloads last month: 28

Total file size:

7.1 GB