The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
adapter_config_sha256: string
adapter_model_sha256: string
adapter_uri: string
command: list<item: string>
child 0, item: string
dataset: string
dataset_revision: string
failure: null
returncode: int64
runtime_patches: list<item: string>
child 0, item: string
schema_version: int64
shape: struct<dataset_rows: int64, group_size: int64, groups_per_batch: int64, max_generate_length: int64, (... 13 chars omitted)
child 0, dataset_rows: int64
child 1, group_size: int64
child 2, groups_per_batch: int64
child 3, max_generate_length: int64
child 4, steps: int64
stage: string
status: string
student: string
student_revision: string
teacher: string
teacher_revision: string
tokenizer_fingerprint: string
validation_dataset: null
validation_revision: null
validation_rows: int64
step: int64
policy_ranks: int64
files: list<item: struct<path: string, size: int64>>
child 0, item: struct<path: string, size: int64>
child 0, path: string
child 1, size: int64
to
{'files': List({'path': Value('string'), 'size': Value('int64')}), 'policy_ranks': Value('int64'), 'schema_version': Value('int64'), 'step': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
adapter_config_sha256: string
adapter_model_sha256: string
adapter_uri: string
command: list<item: string>
child 0, item: string
dataset: string
dataset_revision: string
failure: null
returncode: int64
runtime_patches: list<item: string>
child 0, item: string
schema_version: int64
shape: struct<dataset_rows: int64, group_size: int64, groups_per_batch: int64, max_generate_length: int64, (... 13 chars omitted)
child 0, dataset_rows: int64
child 1, group_size: int64
child 2, groups_per_batch: int64
child 3, max_generate_length: int64
child 4, steps: int64
stage: string
status: string
student: string
student_revision: string
teacher: string
teacher_revision: string
tokenizer_fingerprint: string
validation_dataset: null
validation_revision: null
validation_rows: int64
step: int64
policy_ranks: int64
files: list<item: struct<path: string, size: int64>>
child 0, item: struct<path: string, size: int64>
child 0, path: string
child 1, size: int64
to
{'files': List({'path': Value('string'), 'size': Value('int64')}), 'policy_ranks': Value('int64'), 'schema_version': Value('int64'), 'step': Value('int64')}
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.
Native Tinker-style reproduction: retained trace archive
This archive contains the retained student evaluation generations, not a complete training trace. It accompanies three public PEFT adapters: SFT step 400, one-step OPD from SFT step 400, and last SFT checkpoint, step 2888.
evaluations/sft/step-*/aime_2024.jsonl holds one-sample AIME 2024 generations for 40 SFT checkpoints (steps 2–24 every two steps, then 100–2800 every 100 steps). evaluations/opd-step1/aime_2024.jsonl holds the one-step OPD evaluation generations. Each directory also has the original result summary and evaluation manifest. The manifests pin the evaluator command, model revision, sampling settings, and score. These data were copied from the committed CoreWeave object-store outputs, not regenerated.
training/opd-step1-manifest.json preserves the exact trainer command and teacher route. training/opd-step1-checkpoint-commit.json inventories the completed checkpoint. These are run metadata, not teacher-score traces.
Teacher trace gap: the one-step OPD training job used local teacher Qwen/Qwen3.5-9B to score the student-chosen tokens, but it did not retain token-level teacher log-probabilities, teacher responses, or student training rollouts in its output root. The training manifest and source snapshot specify the teacher and exact scoring path, but cannot recover the historical values. No teacher trace is claimed or fabricated here. To retain both sides in a future run, enable explicit training-trajectory and teacher-evidence dumps before launch; that would be a new experiment, not a recovery of this one.
The independent AIME result for the one-step OPD adapter was 26/30 raw correct, with two length truncations. Its manifest sets aime24_comparable=false. SFT step 400 scored 19/30, with no truncations. The raw records are here so readers can audit those claims; the published Tinker cookbook used a separate service and evaluator protocol.
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