The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
data_source: string
prompt: list<element: struct<content: string, role: string>>
child 0, element: struct<content: string, role: string>
child 0, content: string
child 1, role: string
response: list<element: struct<content: string, role: string>>
child 0, element: struct<content: string, role: string>
child 0, content: string
child 1, role: string
ability: string
reward_model: struct<ground_truth: string, style: string>
child 0, ground_truth: string
child 1, style: string
extra_info: struct<index: int64, split: string>
child 0, index: int64
child 1, split: string
responses: list<element: string>
child 0, element: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1100
to
{'data_source': Value(dtype='string', id=None), 'prompt': [{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}], 'ability': Value(dtype='string', id=None), 'reward_model': {'ground_truth': Value(dtype='string', id=None), 'style': Value(dtype='string', id=None)}, 'extra_info': {'index': Value(dtype='int64', id=None), 'split': Value(dtype='string', id=None)}, 'responses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2285, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1879, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
for key, pa_table in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
data_source: string
prompt: list<element: struct<content: string, role: string>>
child 0, element: struct<content: string, role: string>
child 0, content: string
child 1, role: string
response: list<element: struct<content: string, role: string>>
child 0, element: struct<content: string, role: string>
child 0, content: string
child 1, role: string
ability: string
reward_model: struct<ground_truth: string, style: string>
child 0, ground_truth: string
child 1, style: string
extra_info: struct<index: int64, split: string>
child 0, index: int64
child 1, split: string
responses: list<element: string>
child 0, element: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1100
to
{'data_source': Value(dtype='string', id=None), 'prompt': [{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}], 'ability': Value(dtype='string', id=None), 'reward_model': {'ground_truth': Value(dtype='string', id=None), 'style': Value(dtype='string', id=None)}, 'extra_info': {'index': Value(dtype='int64', id=None), 'split': Value(dtype='string', id=None)}, 'responses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
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.
In view of the significant performance improvements recently demonstrated by the Qwen3 series of models, we conducted a comprehensive evaluation of their capabilities across a range of representative benchmarks. Specifically, we evaluated the Qwen3 models on AIME2024, AIME2025, and GPQA Diamond. The prompt format used in these experiments is provided in the response files; additional details regarding the prompt design will be presented at a later time. Each set of inference experiments typically requires approximately two weeks of GPU compute time. To avoid redundant computations and facilitate community access, we release the complete set of inference results alongside this report. We welcome any questions or further discussion related to these settings.
🦾 Inference Setting
All experiments were performed using 4XA100-80GB GPUs, with a maximum response length of 32k tokens. Each prompt was rolled out 64 times to account for variability in the responses. A model took about 2 weeks to complete all reasoning tasks on three data sets.
📝 Related link of our test results:
DeepSeek-R1-0528-Qwen3-8B-Rollout64-32k-AIME2024-AIME2025-GPQA
Qwen3-8B-Rollout64-32k-AIME2024-AIME2025-GPQA
Qwen3-4B-Rollout64-32k-AIME2024-AIME2025-GPQA
📚 Citation
@misc{su2025WhatelsecanQwen3do,
title={What else can Qwen3 do?},
author={Su, Xuerui and Wang, Zun},
url={https://huggingface.co/datasets/Xuerui2312/DeepSeek-R1-0528-Qwen3-8B-Rollout64-32k-AIME2024-AIME2025-GPQA},
year={2025},
}
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