The dataset viewer is not available for this subset.
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.
MCTD training resources
Versioned resource packs for the MCTD github_release training/evaluation code.
Extract paths are relative to the release root. Use its
python scripts/download_resources.py --environment all --with-pretrained
instead of manually moving files. resources.json records SHA-256, sizes,
destinations and quality flags for every file. These archives are not an
automatic Hugging Face load_dataset table.
| Teacher dataset | Successful episodes | Steps |
|---|---|---|
| ALFWorld | 3,553 | 37,471 |
| ScienceWorld | 874 | 17,302 |
| PlanCraft | 1,145 | 8,839 |
| EB-Habitat (custom text-only) | 1,720 | 14,989 |
These are collected and hindsight-supplemented trajectories retained by the
project's validated export. Publication verifies JSON parsing, index counts,
and success=true, done=true; it does not independently replay each episode.
Only use training tasks for gradient updates. The repository also contains
student-compatible command tables and PlanCraft official-planner trajectories.
Small official resources and evaluation protocols are shipped with the code.
Supervision quality and provenance
Successful task completion does not establish that every reasoning field is correct or complete. Preserve and honor per-step quality/availability flags. Do not treat absent curves as teacher probabilities or invent missing E+C.
| Dataset | Hindsight/answer-conditioned steps | Truncated reasoning steps | Known missing E+C steps |
|---|---|---|---|
| ALFWorld | 10,149 | 31 | 0 |
| ScienceWorld | 1,677 | 96 | 379 |
| PlanCraft | 6,848 | 114 | 0 |
Some legacy eligibility flags remain true on truncated steps (30 ALFWorld, 90 ScienceWorld, 1 PlanCraft); consumers must also check the quality flags. The original manifests and supervision payloads are preserved, not repaired or re-generated during upload. Model tags used for dataset management are not necessarily the actual generator identity. The data combines multiple collection/supplementation runs and model backends; consult generator and provenance fields rather than treating all annotations as a single Qwen run. Intermediate E+C may be student-scored rather than teacher-scored.
External resources and rights
Base model weights are not hosted here. Obtain Qwen3-1.7B and Qwen3-30B-A3B-Instruct-2507 from the official Qwen repositories. ALFWorld raw task runtime archives are downloaded directly from the official ALFWorld releases, not mirrored here. Upstream references: ALFWorld, ScienceWorld, PlanCraft. Environment-derived material remains subject to its upstream terms, and generated content to applicable provider terms. This card does not grant a new blanket license or assert ownership of third-party environments/content.
This snapshot is for reproducible research, not a new benchmark split or an independent accuracy claim. It contains no API credentials or optimizer states.
Habitat extension (2026-09-09)
The Habitat package adds 1,720 successful trajectories across 254 training tasks, the immutable 300-task protocol (254 train / 46 test), the 70-action vocabulary, and verified expert records. This is a custom text-only, outcome-selected protocol, not the official visual EB-Habitat split. Preserve simulator episode mappings. The dataset contains 75 collected and 1,645 hindsight trajectories; original provenance and quality fields are retained. Its manifest reports E+C for all 1,720 episodes, not an independent guarantee of the correctness of each annotation.
The raw official model-rollout archive is available from
EmbodiedBench/EB-Habitat_trajectory_dataset
at revision d391eba8684b0de0c4f37a223e69237c91444209.
Simulator sources and ReplicaCAD/YCB/Fetch assets are fetched from pinned official
upstreams by scripts/setup_conda.sh eb-habitat; they are not mirrored here.
No failed raw-collection workdir, empty command-table placeholder or credentials
are included. Latent SFT constructs its real command table from the installed
student once. Use python scripts/download_resources.py --environment eb-habitat.
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