The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
problem: string
solution: string
answer: string
subject: string
level: int64
unique_id: string
url: string
id: int64
question: string
to
{'id': Value('int64'), 'problem': Value('string'), 'solution': Value('string'), 'answer': Value('string'), 'url': Value('string'), 'question': Value('string')}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
problem: string
solution: string
answer: string
subject: string
level: int64
unique_id: string
url: string
id: int64
question: string
to
{'id': Value('int64'), 'problem': Value('string'), 'solution': Value('string'), 'answer': Value('string'), 'url': Value('string'), 'question': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OPSD Expert Trajectories v1
Public, versioned data bundle for expert-conditioned on-policy self-distillation and mathematical reasoning evaluation.
train.parquet contains 46,047 examples with columns problem, solution,
ground_truth, and example_id. The solution field contains the complete
expert trajectory. Text and row order are preserved during preparation.
The evaluation files are eval/<name>/test.jsonl:
| Name | Questions |
|---|---|
| math_oai | 500 |
| minerva_math | 272 |
| olympiadbench | 675 |
| aime24 | 30 |
| amc23 | 40 |
data_manifest.json records row counts, column mapping, and SHA256 checksums.
Evaluation files retain their original contents and per-row attribution.
This bundle does not grant new redistribution rights for its underlying data.
Download without authentication:
HF_ENDPOINT=https://huggingface.co hf download xychao/opsd-expert-v1 \
--repo-type dataset --local-dir data
Point your local OPSD experiment configuration's data_dir at the downloaded
directory. Model weights and training code are distributed separately.
Clean training snapshot (2026-11-05)
The training pool now uses the 46,047-row cleaned Numina reference subset. It derives from the previously extracted 48,006-row pool: 1,579 empty-answer rows, 288 duplicate-question rows, and 92 rows from conflicting or unverified duplicate-answer groups were excluded. This is a change of training snapshot, not simply truncating the previous 46,418-row file.
The problem, reference solution, answer text and clean-subset order are preserved. Existing example IDs are retained for matching questions after whitespace normalization; new questions receive deterministic SHA256-based IDs. The manifest records the exact counts and hashes. Cleaning does not certify every reasoning step as correct.
Training source lineage: NuminaMath-CoT,
historical revision 9d8d210c9f6a36c8f3cd84045668c9b7800ef517, via the selected
expert references in xychao/MTG.
The predecessor of this bundle is revision
9ca03f44fe47223552be9521b998ef4e71903265 of this repository.
To load only training data, use the explicit file selection:
from datasets import load_dataset
train = load_dataset("xychao/opsd-expert-v1", data_files="train.parquet", split="train")
Pin revision to the published commit for reproducible experiments.
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