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
schema_version: int64
shard_index: int64
shard_count: int64
stage: string
rl_model: string
rl_data: string
subset_size: int64
subset_selection: string
samples_per_question: int64
trajectory_filter: string
max_trajectories_per_question: int64
sampling: struct<temperature: double, top_p: double, max_tokens: int64, seed: int64>
child 0, temperature: double
child 1, top_p: double
child 2, max_tokens: int64
child 3, seed: int64
generated_trajectories: int64
correct_trajectories: int64
teacher_accuracy: double
truncated: int64
truncation_rate: double
selected_trajectories: int64
selected_questions: int64
questions_with_no_correct: int64
mean_response_length: double
snapshot_sha256: string
shards: int64
rl_model_revision: string
rl_data_revision: string
to
{'schema_version': Value('int64'), 'stage': Value('string'), 'shards': Value('int64'), 'rl_model': Value('string'), 'rl_model_revision': Value('string'), 'rl_data': Value('string'), 'rl_data_revision': Value('string'), 'subset_size': Value('int64'), 'subset_selection': Value('string'), 'samples_per_question': Value('int64'), 'trajectory_filter': Value('string'), 'max_trajectories_per_question': Value('int64'), 'sampling': {'temperature': Value('float64'), 'top_p': Value('float64'), 'max_tokens': Value('int64'), 'seed': Value('int64'), 'system_prompt': Value('string')}, 'generated_trajectories': Value('int64'), 'correct_trajectories': Value('int64'), 'teacher_accuracy': Value('float64'), 'selected_trajectories': Value('int64'), 'selected_questions': Value('int64'), 'truncation_rate': Value('float64')}
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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
schema_version: int64
shard_index: int64
shard_count: int64
stage: string
rl_model: string
rl_data: string
subset_size: int64
subset_selection: string
samples_per_question: int64
trajectory_filter: string
max_trajectories_per_question: int64
sampling: struct<temperature: double, top_p: double, max_tokens: int64, seed: int64>
child 0, temperature: double
child 1, top_p: double
child 2, max_tokens: int64
child 3, seed: int64
generated_trajectories: int64
correct_trajectories: int64
teacher_accuracy: double
truncated: int64
truncation_rate: double
selected_trajectories: int64
selected_questions: int64
questions_with_no_correct: int64
mean_response_length: double
snapshot_sha256: string
shards: int64
rl_model_revision: string
rl_data_revision: string
to
{'schema_version': Value('int64'), 'stage': Value('string'), 'shards': Value('int64'), 'rl_model': Value('string'), 'rl_model_revision': Value('string'), 'rl_data': Value('string'), 'rl_data_revision': Value('string'), 'subset_size': Value('int64'), 'subset_selection': Value('string'), 'samples_per_question': Value('int64'), 'trajectory_filter': Value('string'), 'max_trajectories_per_question': Value('int64'), 'sampling': {'temperature': Value('float64'), 'top_p': Value('float64'), 'max_tokens': Value('int64'), 'seed': Value('int64'), 'system_prompt': Value('string')}, 'generated_trajectories': Value('int64'), 'correct_trajectories': Value('int64'), 'teacher_accuracy': Value('float64'), 'selected_trajectories': Value('int64'), 'selected_questions': Value('int64'), 'truncation_rate': Value('float64')}
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.
DeepScaleR-1.5B teacher rollouts (frozen sampling)
Fixed distillation snapshot produced by sampling the frozen published RL checkpoint
agentica-org/DeepScaleR-1.5B-Preview (revision e3f524ce…) on its own published RL data
agentica-org/DeepScaleR-Preview-Dataset (revision b6ae8c60…).
The RL model was never updated — this is ordinary inference.
Construction
| questions | 4,096, selected deterministically by sha256(normalized_problem) lexicographic order |
| samples/question | 32 |
| prompt | rllm DEEPSEEK_MATH_SYSTEM_PROMPT, as a system message |
| sampling | temperature 0.6, top_p 0.95, max_tokens 32768, seed 20260713 |
| verifier | slime.rollout.rm_hub.deepscaler |
| filter | correct only, capped at 8 per question |
Measured
generated 131,072 trajectories
correct 81,373 (teacher accuracy 62.1%)
selected 25,549 covering 3,481 / 4,096 questions (85.0%)
truncation 1.0% mean response length 4,021 tokens
Subset selection is by content hash rather than a seeded shuffle, so the selection does not depend on the source file's row order and can be verified from the data alone.
Schema
One JSON object per line, gzipped, sharded 8 ways (shards hold disjoint question_ids, so
concatenating them preserves the per-question cap):
question_id, prompt_ids, response_ids, response_loss_mask,
reward: {acc: true}, problem_sha256, answer
This is the schema consumed directly by
soft_prompt_rl.consolidation.
Resulting prompt: namezz/deepscaler-1p5b-soft-prompt-len16
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