Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
epoch: int64
train_loss: double
train_nll: double
train_kl: double
lr_end: double
wall_s: double
resp_tokens: int64
global_step: int64
legal_only: bool
n_legal: int64
legal_mass_mean: double
sampled_illegal: int64
no_legal_pos: int64
peak_alloc_gib: double
peak_reserved_gib: double
tok_per_s: double
seq_per_s: double
holdout_loss: double
holdout_nll: double
holdout_kl: double
holdout_tokens: int64
holdout_legal_mass_mean: double
holdout_sampled_illegal: int64
holdout_no_legal_pos: int64
checkpoint: string
world_size: int64
to
{'epoch': Value('int64'), 'train_loss': Value('float64'), 'train_nll': Value('float64'), 'train_kl': Value('float64'), 'lr_end': Value('float64'), 'wall_s': Value('float64'), 'resp_tokens': Value('int64'), 'global_step': Value('int64'), 'peak_alloc_gib': Value('float64'), 'peak_reserved_gib': Value('float64'), 'tok_per_s': Value('float64'), 'seq_per_s': Value('float64'), 'holdout_loss': Value('float64'), 'holdout_nll': Value('float64'), 'holdout_kl': Value('float64'), 'holdout_tokens': Value('int64'), 'checkpoint': Value('string'), 'world_size': 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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
epoch: int64
train_loss: double
train_nll: double
train_kl: double
lr_end: double
wall_s: double
resp_tokens: int64
global_step: int64
legal_only: bool
n_legal: int64
legal_mass_mean: double
sampled_illegal: int64
no_legal_pos: int64
peak_alloc_gib: double
peak_reserved_gib: double
tok_per_s: double
seq_per_s: double
holdout_loss: double
holdout_nll: double
holdout_kl: double
holdout_tokens: int64
holdout_legal_mass_mean: double
holdout_sampled_illegal: int64
holdout_no_legal_pos: int64
checkpoint: string
world_size: int64
to
{'epoch': Value('int64'), 'train_loss': Value('float64'), 'train_nll': Value('float64'), 'train_kl': Value('float64'), 'lr_end': Value('float64'), 'wall_s': Value('float64'), 'resp_tokens': Value('int64'), 'global_step': Value('int64'), 'peak_alloc_gib': Value('float64'), 'peak_reserved_gib': Value('float64'), 'tok_per_s': Value('float64'), 'seq_per_s': Value('float64'), 'holdout_loss': Value('float64'), 'holdout_nll': Value('float64'), 'holdout_kl': Value('float64'), 'holdout_tokens': Value('int64'), 'checkpoint': Value('string'), 'world_size': 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.
Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors
Teacher-generated transfer data and related artifacts for the paper Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors (Jan Dubiński, Anna Sztyber-Betley, Jan Betley, Owain Evans, 2026).
Code: https://github.com/TruthfulAI-research/beyond_owls
| Folder | Paper section | Contents |
|---|---|---|
6_backdoor/ |
Section 6, Appendix D | 4.5 M number-sequence completions of a Qwen3.5-9B teacher steered to answer in French after a female name; the training set of the sampled-token (SFT) student |
7_chess_hacking/ |
Section 7, Appendix E | Number-sequence prompts and the top-32-logprob generations of a Qwen3.6-27B teacher steered to hack at chess (and of the unsteered and random-vector control teachers), the steering vectors, and per-episode ctfish outcome summaries |
- Downloads last month
- 1