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
model: string
benchmark: string
thinking: bool
runs: int64
tasks_per_run: int64
run_correct: struct<1: string, 2: string>
child 0, 1: string
child 1, 2: string
mean_accuracy: double
combined: string
time: double
sampling: struct<temperature: int64, top_p: double, max_new_tokens: int64, request_seed: null, thinking: bool, (... 53 chars omitted)
child 0, temperature: int64
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, request_seed: null
child 4, thinking: bool
child 5, engine_seed: int64
child 6, request_interval_seconds: int64
agent: struct<name: string, hint: bool, thinking: bool, note: string>
child 0, name: string
child 1, hint: bool
child 2, thinking: bool
child 3, note: string
campaign: string
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
served_model_name: string
endpoint: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'thinking': Value('bool'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'hint': Value('bool'), 'thinking': Value('bool'), 'note': Value('string')}, 'sampling': {'temperature': Value('int64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'request_seed': Value('null'), 'thinking': Value('bool'), 'engine_seed': Value('int64'), 'request_interval_seconds': Value('int64')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': 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 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
model: string
benchmark: string
thinking: bool
runs: int64
tasks_per_run: int64
run_correct: struct<1: string, 2: string>
child 0, 1: string
child 1, 2: string
mean_accuracy: double
combined: string
time: double
sampling: struct<temperature: int64, top_p: double, max_new_tokens: int64, request_seed: null, thinking: bool, (... 53 chars omitted)
child 0, temperature: int64
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, request_seed: null
child 4, thinking: bool
child 5, engine_seed: int64
child 6, request_interval_seconds: int64
agent: struct<name: string, hint: bool, thinking: bool, note: string>
child 0, name: string
child 1, hint: bool
child 2, thinking: bool
child 3, note: string
campaign: string
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
served_model_name: string
endpoint: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'thinking': Value('bool'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'hint': Value('bool'), 'thinking': Value('bool'), 'note': Value('string')}, 'sampling': {'temperature': Value('int64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'request_seed': Value('null'), 'thinking': Value('bool'), 'engine_seed': Value('int64'), 'request_interval_seconds': Value('int64')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': 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.
Qwen3.8-27B — Android Lab — THINKING MODE
Two runs of the native Qwen3VLAgent on Android Lab (138 tasks, 9 apps) with
thinking mode enabled (vLLM --default-chat-template-kwargs '{"enable_thinking":true}').
25 rounds max, parallel 8, temperature 0, top_p 0.7, max_new_tokens 8192 (thinking needs the
budget; the non-thinking baseline used 2048). The harness parses only the post-</think>
segment of each response with the stock parser; raw responses (including the full thinking
text) are preserved in the traces. There is no task seed in Android Lab: instructions are
identical across runs; the two runs differ only by run-to-run nondeterminism.
Judge: gemini-3-flash for query tasks; rule-based for operation tasks.
Non-thinking baseline: GUI-Gym/qwen3.8-27b-android-lab (52/138 and 53/138, mean 38.04%).
| Run | Correct |
|---|---|
| 1 | 79/138 |
| 2 | 78/138 |
Mean accuracy: 157/276 = 56.88%.
run_N/config.yaml— native eval config (agent, sampling, docker image, AVD).run_N/scores/—results.jsonl(per-task judge/rule outcome) andtotal.jsonl(app totals).run_N/trajectories.tar.gz— native traces (per-step prompt/screenshots/parsed actions AND the full thinking text of every step).two_runs/— combined episodes, protocol, summary.
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