Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AutoEnvScaling — research artifacts
Supporting artifacts for the AutoEnvScaling study of agentic environment authoring: the environments three authoring arms produced, the episodes that produced them, and the runs used to evaluate them. Analysis code, figures and reports live in the companion repository simonucl/auto-envscaling; this dataset holds the material that is too large for Git.
Archived 2026-09-29 from the working storage, which is being decommissioned.
Contents
| Archive | Uncompressed | What it is |
|---|---|---|
authored-environments-20260929.tar.gz |
~1 GB | The corpus the paper rests on. 7,897 authored environment packages — instruction, Dockerfile, verifier tests, reference solution and task metadata for each — produced by eight author models across three authoring arms (prompt, prompt + feedback, harness). Text files only; binaries are excluded. Paths match the package column of analysis/harness_quality_20260918/records.csv in the repository, relative to the storage root. |
failure-study-e4-20260915.tar.gz |
1.3 GB | The raw workspace of the e4 failure study: 72 authoring episodes over 12 cases with two authors, their shipped environments, and the failing base-solver runs they were seeded from. A readable, redacted extract of the same study is in the repository under analysis/qualitative/. |
fig4-proposer-lanes.tar.gz |
~3.5 GB | Raw authoring episodes for the base vs distilled proposer comparison (Qwen3.6-35B-A3B). The per-episode gate reports and the analysis are in the repository; these are the underlying run outputs. |
hilbench-raw.tar.gz |
690 MB | HiL-Bench trajectories and run logs. Metrics, results and training records are in the repository under evals/hilbench/. |
Provenance and caveats
- Environments were admitted by a fixed gate sequence (structure, build, reference solution reaches
reward >= 1.0, no-op scores < 0.5). Admission status per package is in the repository's
records.csv, not in these archives. - The August cohorts' container images were lost with their registry; the repository documents how their base images were reconstructed from TMax recipes and re-validated.
- Archives contain agent transcripts and tool output. They were scanned and scrubbed for credential-shaped strings before upload; if you find anything that looks like a live secret, please open a discussion and it will be removed.
- Model weights are not included. The RL training checkpoints are ~510 GB per iteration in torch distributed-checkpoint format and were not published.
Citation
See the companion repository for the paper and the analysis that reads these artifacts.
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